899: Landing $200k+ AI Roles: Real Cases from the SuperDataScience Community, with Kirill Eremenko
01:33:12 Share

This is episode number 899 with Kirill Aromenko, founder and CEO of Superdata Science. Today's episode is brought to you by Adverity, the conversational analytics platform. And by the Dell AI Factory with NVIDIA.

Welcome to the Super Data Science Podcast, the most listened to podcast in the data science industry. Each week, we bring you fun and inspiring people and ideas exploring the cutting edge of machine learning, AI, and related technologies that are transforming our world for the better. I'm your host, John Krohn. Thanks for joining me today. And now, let's make the complex simple.

Welcome back to the Super Data Science Podcast. We've got not so much an interview today as a fun and engaging and hopefully interesting conversation between Kirill Aromenko and myself. We actually intended this to be a short Friday episode, but then ended up having a ton to talk about on air. And so it became a full length Tuesday episode. Many of you will already know Kirill. He's been on this podcast a

Many times in recent years, and he was in hundreds of episodes in a row at the start because he founded the Super Data Science podcast nine years ago, and he hosted the show until he passed me the reins five years ago. He's founder and CEO of superdatascience.com, the e-learning platform that is the namesake of this podcast.

With over 3 million students, that's crazy, he's the most popular data science and AI instructor on Udemy. He holds a master's from the University of Queensland in Australia and a bachelor's in applied physics and mathematics from the Moscow Institute of Physics and Technology. Today's episode is ideal for anyone looking to advance their data science or AI career, or if you're looking to break into a career in this field for the first time.

In today's episode, Kirill details why employers are still testing AI engineers on basic machine learning fundamentals, even for LLM-focused roles. The surprising reason why staying in data science as opposed to developing an AI specialization could actually be the right career move for you. How one developer discovered the hidden age bias in tech recruiting and the simple hack to beat it.

the two critical skill areas that separate amateur AI engineers from the pros commanding huge salaries, and why the back-to-office movement could give you a competitive advantage in landing a top AI role. Are you ready for this magnificent episode? Let's go.

Whoa, special guest today on the podcast, Kirill Aramenko. Welcome back to your podcast. How's it going, babe? Thanks, John. Super excited to be back here. Everything's going well and here, morning in Australia. How about you? How are you going? Yeah, it's afternoon here in New York. The sun is a perfect spot to give me really shadowy lighting in this recording for people watching.

watching the YouTube version. But yeah, great lightning department. And for most of our listeners who are the audio listeners, it doesn't impact them. We've got great audio, great mics, both of us, which is nice. So Kirill, I understand you have something special for the audience today. Well, as always, I always have something special for our audience. Yeah, today we've got five stories of

five people from the Super Data Science platform, from the membership, whom I interviewed recently in the past couple of weeks. And yeah, we'll just go through what they're going through because like learning, machine learning, AI, data science can be a

daunting task, can be an exciting task, but also can be a lonely task. And so for those of you out there that feel that you're going through certain challenges or having certain wins, there'll be something, hopefully something that you can relate to. That loneliness is great for us at the podcast here, right? Because people feel so lonely, they have no choice but to find a data science podcast to listen to and feel a little bit less lonely. Yeah.

It's funny, but it's interesting. It can be bizarre, but indeed, one of the things that we hear on and on again in the community, because we have this intros page when once you become a member, you type in your intro and you say where you're from, why you joined, what your hobbies are, and so on. And one of the most common things that white people join, I would say maybe 40% often,

Of the time I hear or read this comment that they want to feel part of a community of learners, like they're learning these courses and tools and doing these projects. But around them, there's nobody like that. Like at their work, there's nobody else who's learning. They can't feel they can connect with people. And it's actually a real challenge for people to find connection in this space.

It's great. I make the joke, but the platform is great. I'm sure the podcast and the platform are great for people feeling like they're in a community. And by the way, when you say the SuperDataScience platform, we mean SuperDataScience.com, the website. Which might be obvious, but just to absolutely make that crystal clear. That's right.

Yeah. And so Kirill, yeah, tell us about these five. I guess you can start with the first one. Okay. First one. So all of these names are changed for privacy purposes. And our first person is Alex. He's early in his career and he's in his late 20s.

And just recently had a huge win, which is very exciting. He landed a job as an AI engineer. And that's what our catch up was about. We spoke about it.

Basically, this is how his interview went. He applied to many jobs. A lot of them he got rejected or didn't hear anything back. One of them he heard back. They said they were interested to talk to him more, invited him for an interview. It was originally...

far as I remember, it was originally like a screening interview. They said, "We'll be back in touch." He didn't have high hopes for that because that's what usually happens, but they invited him for a technical interview. Basically, what he did to prepare for the technical interview, he said... In the membership platform, for those who don't know, we have lots of courses and he went through our

LLM, Large Language Models A to Z course again, just in case to refresh some concepts.

And on the interview, they asked him specific questions about their data in the company and how he would create an LLM that, let me find the quote here. How would you create an LLM that can access our data and just answer questions based on that data? And from there, he basically just went into brainstorming mode and said,

gave them ideas of how he would fine-tune existing models, apply RAG and other feature engineering, other things that would be relevant in that case. And another thing that really helped him to

was that he did a few, no, I think he did one collaborative project in the platform. That's where we get like members to work together on projects. And, you know, that experience of going through a real world project helped him like be able to brainstorm on the interview and they were really impressed. Cool. That's great. Congrats to quote unquote Alex. Yeah, yeah.

Did you anonymize any other demographic data? Like, have you switched genders on people? Yeah, in some cases, yeah. Oh, really? So you've really anonymized? Yeah, as much as I could, you know, to keep people's privacy. Even though Alex, in this case, actually gave us full permission to disclose his story. You can actually read about it on Twitter.

the super day science website. Nonetheless, uh, the other thing I wanted to point out was that interestingly, he added that after asking about large angle models, guess what they asked him. They asked him about fundamentals of machine learning. How are you going to build a regression? How are you going to, for like predicting price, how are you going to build a classification, uh, for predicting, um, you know, problems relating to client data. They didn't spend too much time on it, but, um,

Apparently they wanted to know that he knows the fundamentals. What do you think about that? You can't be senior in a job. Let's say, I'm sure there's an entry-level AI engineer role where you really are just using APIs, calling LLMs, but

I think in a lot of senior roles, if you're going to be making a big impact in your organization, you need to understand simpler machine learning models. Ideally, not even just the fundamentals of machine learning, but also the foundational principles that underlie it, like linear algebra and partial derivative calculus. There's all kinds of magical things you can do if you understand that stuff. You can cut through the abstractions,

and come up with really performant solutions that otherwise wouldn't be possible. A career in data science is an endless opportunity to be learning more and more and more. You should never let all the possibilities of the things you could be learning prevent you from getting going, getting some job applications off the ground,

you should go for it. Yeah, for sure. But in this current where we are in terms of AI, it feels to me like AI, engineering, LLM engineering, etc., all those roles, they're kind of like

going through that same arch that data science was going through like 10 years ago. So it's very blurry at the moment. What does AI engineer mean to you? Yeah, it's an interesting thing because theoretically, up until two years ago, if you said you were an AI engineer, that probably meant that you were like an AI researcher. So maybe at a frontier lab,

like Meta, Google, OpenAI, Anthropic, and your engineering. You're figuring out how to get transformers to work together more quickly across a bunch of GPUs, how to automatically clean up data in some way that it improves the outputs of a trained large language model. That's what I would have

up until recently, up until a couple years ago, thought an AI engineer was. But now it seems to mostly mean somebody who is using existing LLMs and calling APIs. And so there's still thoughtfulness that needs to go into making sure that data are clean and consistent and you have guardrails up. But it's your...

You're working at a more abstract level, at least in the simplest way of thinking about the job. So do you reckon you could get that job done without any knowledge whatsoever of fundamentals of machine learning or even the underlying deep learning tensor? What's it called?

Yeah, TensorFlow, PyTorch. Yeah, TensorFlow, PyTorch, but even transformer architectures. Oh yeah, oh yeah. I mean, you could definitely, you know, for that, you know, like I'm saying, you could get, yeah, there's lots of LLM jobs out there where you basically need to, you know, you need to understand how to be evaluating data that are going in as inputs and outputs. You know, you need to be able to do some

exploratory data analysis in Python, those kinds of data science skills. But you wouldn't necessarily need to understand how stochastic gradient descent works or reinforcement learning works. Just because those kinds of approaches were used to train the LLM that you're using, it's probably more important to just have lots of experience with prompting LLMs and seeing what they can do, understanding, experimenting with, okay, if I use a three billion parameter LLM

How does that perform relative to using the latest and greatest Cloud 4 from Anthropic? There might be fine-tuning involved, so understanding the approaches that exist out there

for fine tuning. So things like LoRa, low rank adaptation, being aware of those kinds of things to be able to take a three billion parameter open source Lama model from Meta and then be able to fine tune it to some specific task. You might actually be able to with a three billion parameter model running on your own infrastructure or running through some cloud provider like Hugging Face or PyTorch Lightning. You can have

this very small LLM running on some small, on some very specific task or some, uh, small number of very specific tasks. And because you fine tuned it, fine tuned it to those tasks, it can outperform the latest and greatest like cloud four. And so the, those are the kinds of things, you know, that kind of like empirical experience with playing around with LLMs, um, is probably more important. You know, like you don't,

really need to understand how LoRa works to use LoRa. And by the way, that LoRa, it's L-O-R-A if you're Googling that. I'll try to remember to put... Low-rank adaptation. I'll try to remember to put a link in the show notes to our episode on LoRa, which is back in episode 674. I kind of give an introduction to fine-tuning LoRa. Yeah, I remember that one. That was a great introduction.

Yeah, interesting. The analogy that comes to my mind is I love cooking. And for me, it's like, let's say for cooking, you're using a blender or other tools like an oven and things like that. You don't need to know how an oven works in the backend or a microwave or...

That's lazy cooking. Or a blender. You don't need to be able to pull apart a blender and put it back together, but you can use it. So same thing here with like AI is going towards that direction where these...

tools are actually just tools. You can get away and in fact, as you said, you don't have to go into the depths of understanding these tools to be able to use them. And I'm just wondering, what percentage of jobs

are going to be for AI engineers who know how to use these tools versus the percentage of jobs that where you actually need to understand the underlying technology and be able to tinker with it. Like what should people be focused on learning? I know it depends on their interest, but in terms of like supply or demand for jobs, I wonder how it's going to play out in the coming months and years.

This episode is sponsored by Adverity, an integrated data platform for connecting, managing, and using your data at scale. Imagine being able to ask your data a question, just like you would a colleague, and getting an answer instantly. No more digging through dashboards, waiting on reports, or dealing with complex BI tools. Just the insights you need, right when you need them.

With Adverity's AI-powered data conversations, marketers will finally talk to their data in plain English, get instant answers, make smarter decisions, collaborate more easily, and cut reporting time in half. What questions will you ask? To learn more, check out the show notes or visit www.adverity.com. That's A-D-V-E-R-I-T-Y dot com. Yeah, I think we move more and more towards the abstract. You know, you don't...

I love that analogy that you just gave with the blenders and microwaves and stuff. You can more and more rely on the abstractions, but I still think you can probably get, ultimately, as you progress further in your career, I think you can get higher paying roles. It depends on exactly which way you go because you could say, okay, you know what I'm going to do? I'm riffing here, by the way, Carol. This is just my thoughts, but I'm thinking...

If you want to focus on commercial impact, you could actually say, you know what, I'm going to use training like there is available in superdatascience.com and use that to become proficient at building LLMs. And I'm going to figure out how to make those look nice in a Gradio app or something, some kind of user interface you can quickly put together.

so that you can have a click-and-point interface for people to be using your AI models in the background or some kind of AI solution that you come up with through using LLMs. And you might not understand how gradient descent works or fundamentals of machine learning, but

you're able to put together powerful commercial applications, you could do that on a small team or on a big team or just on your own. And you could potentially be enormously successful. In today, as well as more and more, the further we go into the future, the more you will be able to have

agents, teams of agents working on different tasks for you. And, you know, you could build a big business. You could be a solo entrepreneur and have like hundreds of agents working on different tasks for different clients. And that'll get better and better.

And, you know, so you could potentially have a lot of success that way using just the abstractions. But where I was originally going before I thought of that second idea is that you similarly, you know, you could as you advance in your career, you could say, OK, I'm going to I'm going to peel back layers of the onion more and more. I'm going to understand what's going on under these abstractions more and more and kind of just

chip away over years, over decades, you become more and more expert at understanding machine learning fundamentals and mathematics and physics and engineering, maybe electronics. There's all kinds of related fields that you could dig more and more into. And as you dig more and more, your value is

your clients, your users, your employer, I think does increase. And I think that that will continue to be the case in the future, even though those kinds of things like doing mathematics, being able to solve data science problems, computer science problems, even though that's something that LLMs will be able to do more and more and more,

I think there will still be, and maybe I'm just a dinosaur with outdated ideas, but I think if you understand that stuff, one, it's interesting. It's kind of an interesting thing here because if you're a chef who's able to understand how a microwave works...

and make a better microwave that somehow you can put raw pizza ingredients in there and it turns into this great pizza oven cooked pizza in just 10 seconds. That kind of magic would only be possible if you learned the nuclear physics of how the microwave works. There's magic and possibility if you do dig deep.

Yeah, so I think either way, you know, you can follow your passions. Like if your passions are deep in the nitty gritty of what's underlying machine learning models, I think you can have a huge amount of success there, the more and more you learn. But equally, if you're more interested in applications and just making a big impact and you want to stay with abstractions, you can also have a lot of success that way too. Yeah, yeah. It feels like that, um,

like a transitionary period or like that intermediate period. Like I love your example about the chef who knows how to make a new microwave, but I don't think there's a single chef like that on the planet. Yeah. But for AI, it's the case. I mean, who knows? Yeah. I mean, there probably isn't, but it's kind of like, that's, you know, if you're, if you're the, you know, to, to give an example,

We recently, at the time of you and me recording this episode, I'm not sure the episode will be out or not yet, but I recently recorded a Long Tuesday episode with Sean Johnson, who is a renowned AI investor in San Francisco. And we were talking in that episode about how there's only a few thousand people in the world that are at the cutting edge of AI. And

Those people are typically more interested. These are the AI researchers that up until a couple years ago you might have called AI engineers. They are like the chefs who know how to take apart a microwave. They can be in a big organization

And they don't necessarily need to worry about the downstream commercial impact. They just need to worry about making a better microwave. And by focusing on their piece, I mean, they can be making a seven-figure base income. So that is making an impact.

Yeah, whereas, yeah, it's kind of interesting. I guess like a chef, maybe the incentives aren't aligned as much for them to be learning nuclear physics. I think we've got to also think about the quantity of jobs. There's definitely space for people making better microwaves and AI, but what is currently and what is going to be the majority of demand from employers around the kind of people they employ? And in my view, it's really...

It's really hard to tell at this stage, but from anecdotal evidence like Alex's story, I can see that even though this abstraction layer is gaining popularity, employers still want to hedge their bets and want to vet their candidates by

requiring that you know the fundamentals of machine learning. They're not ready yet fully for candidates that are just operating on the abstraction layer without an understanding of the underlying fundamentals. And I think part of that, I'm thinking about this from the perspective of a hiring manager for a role like that. If you were to hire somebody who had spent just a couple months

learning about LLMs, prompts, inputs, outputs, and didn't have an understanding of anything beneath it, the barriers to entry there are relatively low. You're able to quickly get up to that point. It's like hedging their bets, like you said, and just trying to make sure that they're hiring somebody that's more well-rounded, has been invested in this space for a while, that are really committed to a career in this area.

I think that's a good summary. Should we move on to number two? Yeah, I thought this might be like a five minute Friday episode. Same, but this conversation is just too interesting. I'm like enjoying this.

All right. The story number two is Ben. They're mid-career, switching from a career in process engineer to data science machine learning. That's their goal. They're in their early 30s. And the interesting thing about Ben is I had a conversation with Ben in August 2024, which makes it like five months plus another five months, like 10 months ago. And

At the time, Ben was interesting. Ben told me that they're learning a lot about AI, machine learning, data science, specifically data science and machine learning. That's their goal of their career.

And that in five months from August 2024, they were going to be, quote unquote, oh, not quote unquote yet. Like basically, they were going to be job ready. They were aiming to be job ready to apply for jobs in that space. You know, five months is a decent amount of time, especially for somebody in process engineering who's also been studying machine learning and data science for the past couple of years.

maybe like a year or so. Interestingly, so when we caught up just recently, Ben's comment was, back then in August, I thought I'd be job ready in five months, but the field evolved faster than I could keep up. And at the moment, Ben feels scattered as job requirements keep shifting. He's looking through these different jobs all the time. And, you know, like they're different to what they were five months ago. So,

He's learning one thing, but then by the time he's finished with that course or that series of courses, job requirements have now changed again. And he feels like he's always playing catch up. Plus on top of that, he's got a full-time job. He's got a personal family commitment. So he can't just like focus eight hours a day on preparing for these things. So, and

And also, you know, like he's got the comfort of the income coming from that job. He just wants to change because that's no longer his interest. He's interested in other things that like he wants to be following his passion. But yeah, that's the kind of...

fearful state we find a lot of people are in these days with AI evolving and machine learning. This whole field evolving so fast that they can't keep up and just can't get a toehold on this whole job application process because things are changing so quickly. Nice. And so what's the kind of... Is there an outcome that we're... So I guess we have different... What is the...

Yeah, so these are just like, yeah, they're like, so each of the stories, so I'm piecing this together just like our listeners as we go. Yeah, yeah, yeah. So each of these five stories just kind of gives us a different glimpse of different kinds of situations that people can be experiencing. Nice. Exactly. And I think a lot of people would be experiencing a similar kind of feeling. I've heard this from several people. This is an actual story, not just like a...

aggregated stories, an actual story of an actual person. But I've heard the same story from other people as well, where

it's evolving so rapidly like think about like even uh llms right like lang chain lang graph were like all all everybody was talking about like a year ago like where are they now like that they're no longer as popular as um the hottest thing right now or what about like um uh what are they called like prompt engineering everybody was talking about prompt engineering like

One and a half years ago, that was the hottest new thing. Now people are talking about MCP, agentic AI, and things like that. What's going to be happening a year from now, we can't predict with certainty at all. I would say you can predict some things. Some things are fundamental. Yes, there are exciting new trends, absolutely. MCP right now, crew AI, those are exciting trends. But simultaneously, there are undercurrents

that you can see long-term and be like, okay, that is a safe thing to be learning. This is going to be valuable to me for a long time. For example, all of those things that we were just talking about happen in Python. And so learning Python is a great skill. And then that also means if you want to go even deeper, you can say, okay, well, learning data structures and algorithms is going to be useful because learning how to make

Understanding the computer science that this Python code is written to be able to do gives you lots of options. So even if we somehow move on from Python, we're not going to move on from Python in the next couple years. But maybe five years, ten years, everyone's using Rust or something. And in that scenario...

it'll still serve you well to understand data structures and algorithms. There's always slower, there's these trends, these megatrends, in the same way that with prompt engineering, that was kind of like, to me, that always seemed like an obvious thing that was going to go away quickly because

All of the frontier labs developing the cutting edge LLMs, they are creating huge data sets and fine tuning LLMs to be better and better at taking whatever prompt goes in and predicting what output someone was looking for. So it becomes less about being like a specialist and oh, how do I like hack this LLM to do what I want? And every six months that goes by, the leading edge LLMs are going to just be like way better at just anticipating your needs.

So yeah, so these kinds of long-term trends. You can bet that microchips are going to be cheaper and cheaper per unit of compute. That's the ultimate megatrend that is making all of this magic happen. And so those kinds of things, you can look for those kinds of big long-term trends and feel confident about some things. So yeah,

I agree with your point that there's definitely always hot new things. And that creates anxiety for sure. But simultaneously, you can find some peace. You can find some stillness in these long-term things like SQL. That's been around for decades and it's not going away. That's a good point. I like that. It probably adds calmness if you separate your learning into...

I guess it's like exploitation and exploration, right? Like you exploit the existing, as you said, megatrends, like 60% of your learning and then 40% of the learning you focus on new hot things. At least you'll have that 60% of grounded calmness and slow, steady progress. Yeah, I like that idea.

Pretty cool. And what you said about the cost of chips going down reminded me of a comment by Sam Altman recently. He said, Sam Altman, that cost of intelligence is going to converge to the cost of energy over time. And yeah, that's kind of, I don't know why I thought of that, but it relates to that. Yeah, and related to that, so...

The Anthropic CEO, Dario Amadei, a few months ago wrote a blog post. You've got to have him on the podcast, man. Oh, yeah, I'd love to. That would be a perfect guest. Yeah, I mean, Sam Aldman, Dario Amadei. Sam, Dario, if you're listening. Bill Gates, the Kardashians. Let's get them all on. Got it. Ed Biden. Joe Biden's retired now. We should get that guy on. Yeah.

So you're saying CEO of Anthropic? Yeah, so Dario Amadei, a few months ago, had a really popular blog post. It's related exactly to this cost of intelligence going down so much that he was describing a situation where in the not-too-distant future you have a data center with a million agents, like a million...

like the equivalent of a million human brains, and those human brains are Nobel Prize winners. And you have these million Nobel Prize winning intelligence brains just in one data center, working away. They don't need to sleep. They don't need to take care of their kids. They're just like... And that's coming. That's going to change the world. And related to the energy comment that Sam Altman made there, what I think is really interesting is

AI, this abundant intelligence, is playing a role in helping us get more energy, clean energy, including things like helping us contain the plasma in a nuclear fusion reactor, which if we can crack that, then all of a sudden you have basically unbounded energy and unbounded intelligence because

So that's a pretty wild world that we could be going into. Incredible. You said Dario, right, is the CEO of Anthropic? Indeed. He strikes me as a bit of a futurist. His comments that by the end of 2025, all code, or 90% of the code, will be written by LLMs, to me, feels a bit far-fetched. Kind of reminds me of

Ray Kurzweil in his predictions, but Ray Kurzweil's predictions mostly come true. So we're yet to see.

This episode of Super Data Science is brought to you by the Dell AI Factory with NVIDIA, delivering a comprehensive portfolio of AI technologies, validated and turnkey solutions with expert services to help you achieve AI outcomes faster. Extend your enterprise with AI and GenAI at scale, powered by the broad Dell portfolio of AI infrastructure and services with NVIDIA industry-leading accelerated computing.

It's a full stack that includes GPUs and networking, as well as NVIDIA AI enterprise software, NVIDIA inference microservices, models, and agent blueprints. Visit www.dell.com slash superdatascience to learn more. That's dell.com slash superdatascience.

And a big part of the way that Ray does that is from that big megatrend that I was talking about of compute cost. Basically, you can model, it's been consistent over decades, what compute costs over time, and you can extrapolate that forward to get the big...

five-year, 10-year, even 15, 20-year predictions that Kurzweil has been making have been based on that hardware basis. I don't know that quote from Dario Amadei about

us having all code be generated by the end of 2025. I mean, that's not true. I guess a key thing to remember is that when Dario or Sam Altman are talking, they're also pitching. Yes, yes. I heard that podcast with you with the guy from LinkedIn, I forgot his name. John Rose, yeah. No, no, not John Rose. The guy

uh the influencer nope nope the the i think uh i forgot um very big following and he said you you guys were talking about the ceo of nvidia talking about like uh the importance of microchips and he mentioned that he's you know you got to remember that he's pitching his company whenever he makes one of these kind of claims oh yeah jensen huang yeah there's another guest that we should just let's phone him oh yeah why not why not yeah

Anyway, what do you think of this advice that I gave to Ben in his situation? Well, he was actually debating that he wanted to get into data science, but he also sees AI as the future. And he was thinking, I'll go into data science from process engineering, which he's currently in. I will learn everything about data science, then I'll move to AI. And I told him that...

He should aim straight for AI. If AI is his end goal, there's no need to go through the path of becoming a data scientist first and then going into AI. Maybe that was the case two years ago when AI was very deep learning, everything you have to know those things. But now there's a straight path to AI. What do you think? Would you recommend that for people as well? 100%. Something that I've talked about on the show, maybe even with you, I can't remember, but...

This feels like a conversation we've had on air before. I think you can kind of think of an AI engineer, an LLM engineer, as being a specialized kind of data scientist. Where 15 years ago when data science was a brand new term and people were starting to do it, there was this kind of...

it hadn't evolved. We didn't have all the kinds of different tools, all the different kinds of specializations. As a data scientist, there's this joke that a data scientist is someone who's not good at statistics or programming. It's like you're able to do a little bit of a bunch of different things. You're able to understand enough about statistics at that time, maybe

maybe machine learning, data and analytics, a bit of SQL, maybe R at that time, some Python, and you have to do some visualizations, presentation skills, getting buy-in from management. Originally, it was kind of this idea that to be a data scientist, you might need a PhD 15 years ago. But now,

But now it's evolved. It's related to the same idea. People talk about how AI could take everyone's job. And maybe there is some timeline where that kind of happens. But the thing that has happened historically with all other automations is that more roles are created. And that's exactly...

LLM engineer could not be more of an embodiment of that truth, which is that because AI is so capable, now all of a sudden you need all these humans to be able to glue together all of those intelligent machines in order to do something that's useful, in order to create a product that provides a solution that's commercially valuable.

And so, yeah, AI has created all of these additional kinds of data science specializations. So now you have your data engineer, your ML engineer, your LLM engineer, your data analyst, your database specialist. Yeah, maybe. Last year. Yeah.

Exactly. So, yeah. Interesting. Yeah. On that topic of creating new jobs, I love what the CEO of Dell said on one of your previous podcasts that never thought of this, that a lot of new jobs will be created in construction, building those data centers and infrastructure for data centers. And that's going to last like a decade, if not decades.

That's huge. Technology impacting the non-tech sector in terms of number of jobs. That's incredible and I love that. That creates opportunities for people who are not even in the tech space. Exactly. That's a huge guest that we had recently. The CTO and Chief AI Officer of Dell, just hundreds of thousands of employees, John Rose. And I think maybe when you were, the other episode we were talking about,

I think that might have been Greg Michelson from Zerve that was talking about. I can find the person. But what I was going to say is such a good episode with the CTO of Dell. Incredible episode. Loved it. So...

It's so well-spoken and also so many great ideas, especially anybody looking for commercial applications of AI and how it applies to business and industry. I'm recommending that episode to people around. Nice. Yeah, it's super popular.

All right, are we on to number four now or what? Yes, yes, almost. I'm just finding this guest's name. Yeah, there we go. Andrej Burkov is who I was talking about. Andrej Burkov, yeah, yeah, yeah. So that's going back a little bit. Yeah, Andrej Burkov, it's completely insane. He has his LinkedIn and newsletter. At the time of recording, it was like 980,000 subscribers or something. He's super close to having a million subscribers.

subscribers to his AI newsletter on LinkedIn. Episode 867, I believe. Yeah, 867. I think people like him because he's just raw, you know, like no filters, just says his opinion. Doesn't matter if he's going to offend people, not offend people. I've seen some of his comments on, they removed my post from this post because they didn't like what I said. Ha ha ha, here's a screenshot of how it looked.

You know, like it's people I know in this day and age when there's so much like, uh, fakeness and not in non-genuine, non-genuine people or non-genuine presentation of themselves. Like, I think people value that the rawness, whether you like it or not, you know, that's a separate question just, but having access to somebody's raw personalities character, I think it's nice.

We were super lucky to have him on the show. He never does videos, podcasts. He writes books, which are extremely popular. And free. Are they free? I think. You have to pay to get the physical version, obviously. Yeah, but then you get the one online for free and you pay however much you valued it for. I was very impressed by that. He's famous for the 100-page machine learning book,

But then he was on the show talking about his 100-page LLM book. And so we'd reached out to him. I'd been reaching out to him through various means over several years, and he never responded. And then when he finished the 100-page LLM book, he reached back out to us, and he was like, can we still do this? And so it was really cool to have somebody like Andre on the show who so rarely does those kinds of appearances. And he does them so rarely that he hears something hilarious. So he never hears recordings of his voice. And so...

He's from Ukraine and so he has this strong Slavic accent. It's not that strong, but he thinks that he sounds like, because he's been living in Montreal for decades, and so he thinks that he has this North American sounding English accent. And so he's like, what the hell? When he listened to the recording, he's like, who is this guy? That's funny. Oh, yes, yes.

um good times i i think it's it's important to hear yourself on a radio on on on air sometimes to understand are you doing a bit or something you sound the same oh nice nice all right let's move on to number three

Okay. Wait, four? Surely four. I wish. Are you kidding me? We're on number three? No, number three. That's right. This is five minute Friday. It's been going for 37 minutes. Maybe we'll have to convert this to a Tuesday episode. Maybe. You're the host. It's your call. I'm just having a fun time. That's the benefit of retiring from the podcast.

I don't have to make these decisions. All right, number three, Clara. She's a senior developer living in LA and in her mid forties, aiming for roles in the 200,000 plus salary range. So this person, she has been working already for two decades or more in this space.

And has tons of experience, in fact, has done all sorts of roles in software engineering, developing apps, developing programs, developing different...

things for different companies. Most recently for the past, I think it was five years, sorry, three or five years. I don't remember. Let's say three years. She has been creating software using Python. Interestingly, software that processes data, lots of Excel, lots of CSV files using Python in the medical space. In fact, a lot of our members, I don't know the exact percentage, but a lot of our members I speak with work in the medical space supporting

companies, whether it's hospitals or pharmaceuticals or other medical space related, like medical equipment companies, procurement companies or supply chain companies and so on. Anyway, so she's been creating all this software using Python, specifically Pandas and other tools to process lots of data. So lots of Python experience.

and recently has done four of our machine learning courses, machine learning A to Z, machine learning level one, machine learning level two, machine learning level three. And she wants to get into the space of machine learning and AI. Why? The reason is because Clara is in her mid forties. She can, she predicts that she'll be in the workforce for at least another 15 years. And she can see that the current role that she's doing is while she's

you know, pays well and she's very good at it. It's not, it might not be as relevant in the future. It's not a role that, um, as we discussed with Clara, it's like, it's not a role that's a self-fulfilling prophecy. She's not learning new skills in the role that will, uh,

open up more doors for her in the future that will keep her growing with the growing trends in technology. She's very selective about applications. In fact, she left her job a few months ago to focus specifically on studying and preparing for the new role. She's not in a rush. She wants to take things slowly and

basically goes mostly through her network, not applying to thousands of jobs through LinkedIn and so on, mostly goes through her networks, very selective.

And yeah, so that's her goal to get into this space. And the interesting thing, the pain point that Clara has is she's finding there are thousands, literally thousands of job applicants per job. And even at her level of experience, expertise and background and all these projects that she's done, she's finding it difficult to break in and to land the job that she's looking for. It's...

Probably related to this...

phenomena where there's lots of jobs, but there's also lots of applicants and it's really hard to stand out. I think it's been the same for the past 10 years when there's lots of people applying through the direct means of just submitting your resume and all of them get pre-screened with AI tools. If the hiring manager had a conversation with Clara directly, magically,

then they would realize she's amazing and they would hire her in a heartbeat. But because it's really hard to get in front of people this direct way, I think that's the problem. And I think Clara's got the right idea of going through connections and going through networking to get in front of the people quicker. What do you think?

Build the future of multi-agent software with Agency, A-G-N-T-C-Y. The Agency is an open-source collective building the Internet of Agents. It's a collaboration layer where AI agents can discover, connect, and work across frameworks.

For developers, this means standardized agent discovery tools, seamless protocols for interagent communication, and modular components to compose and scale multi-agent workflows. Join Crew.ai, LanqChain, Lama Index, Browserbase, Cisco, and dozens more. The agency is dropping code, specs, and services no strings attached. Built with other engineers who care about high-quality multi-agent software.

visit agency.org and add your support. That's A-G-N-T-C-Y dot O-R-G. Networking, ideally in person, is, I think, easily

the best way to get your professional opportunities. Not everyone can do that. You might have a family situation or just where you are geographically. If you want to get a job in data science or AI, maybe there aren't in-person things you can be doing. Remote's really the only option. And there are probably then in that kind of scenario still things like superdatascience.com, these kinds of platforms where you can get involved, you can do

collaborative projects together, get to know people. That gives you that collegial feeling. You'll remember the projects you've worked on, the people you've been with, their expertises. That's something like working with someone in an office and understanding what they can do. Maybe they'll open a door for you some years from now. The more you do that, the more that you're

working with people online if you have to. But ideally, you are meeting people in person. There's something about, in the US, there's something called meetup.com. And with meetup.com, you can in any major city in the US or Canada, you can find meetups for whatever you're interested in. It's not specific to tech.

I'm sure there's like, you know, microwave reprogramming chefs meetup. There's all kinds of specific things out there. But, you know, in data science in particular, you know, there's lots of different of these kinds of meetups and you go and you could be at any stage, you know, you could be just getting started. You could be thinking about, you know, maybe you're like a medical doctor and you're

you're tired of just, you know, dealing with one human at a time and you have a vision for some kind of like medical AI system that you want to build, um, to like scale up your impact. And so you can start going to these meetups and, and meeting people and decide, okay, well maybe like how, you know, how can I take further steps into this? Um, you know, should I be joining a platform like superdatascience.com or like do a master's in person at a local university? Um,

So you could be at that very early stage where you're just exploring if a career in data science or AI is something you're interested in, all the way through to being a big expert

You might participate in giving the talks if you're an expert. Often these meetups have that. It could be based around one or two speakers talking about real-world projects or some open-source library they're developing. You learn stuff from the speakers, but around these you also have lots of social interaction. There's drinks at a lot of these. Pizza is often the food that they order.

And, you know, sometimes it's sponsored by some local data science or AI company, or maybe there's like some small fee, like five bucks or 10 bucks that you pitch in to be able to, you know, buy the pizza and the beer or whatever. And yeah, it's in those social interactions that you, yeah, you meet people and some people just, you know, you click with them and you chat with them more. You see them there a few times and

And yeah, you might find your next job. You might find your romantic partner. You might find your best friend. You never know. In a way that I think those kinds of things, those kinds of connections, it's a little bit harder to make them online, but it can happen. You and me, we're going to meet soon in person for the first time, but we've known each other for over five years. I feel like I know you. You're one of my closest friends, but I've actually only...

talk to you through pixels. Yeah. Well, thank God it happened before all the deep fakes. So we know we, we're real. Yeah. Yeah. Uh, interesting. You mentioned, um, the medical field, like doctors looking to apply, uh,

LLM's AI to scale their impact. I could probably name right now off the top of my head, well, I had to look it up, but at least five people in the Super Data Science platform, we've had five people who in their intros have posted, oh, I am a doctor or I am a nurse. And there's this point in the nursing community

In the hospital databases, there's this problem. We're constantly facing it. I'm eager to solve it. I'm on a mission. We're going to use AI to solve it. That's why I'm learning AI. There's so many opportunities in the medical space to apply AI.

for whether it's scaling impact to patients, improving existing systems processes, improving administration of hospitals and things like that. It's incredible. I never thought, I'm always surprised at how many people are finding like,

active pain points all the time in the medical field. It's by far the highest, the most popular, almost commented on to the point that we even had a quote unquote meetup, a virtual meetup inside Superdesign. It was actually more of like a mentorship session where we hired an expert in

the medical field in, I think it was bioinformatics. And she ran a workshop for people and then they could ask questions and all discuss these kind of things because that's how many, how much demand we had for that space.

Yeah, I mean, you can, you can, I think, you know, people in the medical field, they're, you know, they often they want to be making a big positive impact in the world. They're typically very intelligent people, hardworking people. And so there's a lot of overlap in that Venn diagram with the kind of people who want to be interested in data science and AI. But you can come from any field. I don't know if you've come across this woman, this woman, I hope I'm pronouncing her name correctly, Adriana Salcedo. She is in Bavaria in Germany.

And she's been working as a flight attendant for seven years. And she's actively, her full-time job is flight attendant. And she has been studying, she's been listening to the podcast for a couple years, to this podcast, the Super Data Science Podcast. And she's learning data science skills. And recently, she started posting about, you know, she started posting little projects she's been doing. She's been doing things, she actually recently completed Ed Donner's LLM engineering course, which...

the super data science team was involved in creating. And, uh, yeah, I, I mean, it's a, yeah. So it's, it's, it's really interesting. Should, I mean, there's so many possible topics for podcast episodes, but she's someone that I'm like, it'd be interesting to get her on the show. Yeah. She's, she's been a member of a super data science, uh, for over a year now. Yeah. Oh, she's a member of the platform too. Yeah. Yeah. I didn't even know that. Yeah. Passionate AI enthusiast. Yeah. There you go. Yeah.

Cool. Awesome. Okay. So, oh, one piece of advice I gave Clara, let me know what you think about this, is to, like, Clara's like, all right, I'm a software engineer, senior developer. I'm going to learn all these skills about AI machine learning to then apply for these jobs. And

The advice I gave was work backwards. Like go and talk to some recruiters in LA and ask them what are employers looking for in an AI engineering role? What are their main requirements? What boxes do I need to check for you to be able to get me a job? And then learn those things. And you kind of hit two birds with one stone there. You narrow down your learning scope

And you can focus more deeply on those things. And also, you get, like speaking of in-person networking, you get in front of these recruiters who then now know that you already have a great background. You just need to touch up on a few things. It'll take you a couple of months.

And then you can reach back out to them and say, I'm ready. And it's in the recruiter's best interest to place the best candidates because they get paid a commission for that. So like it's a win-win. You don't have to go looking for jobs. And then these recruiters can, as long as they're like, they have trust in you that you have checked those boxes, they can go and help you get those jobs.

So, you know, like it kind of seems obvious, but sometimes we don't stop to think. We just learn everything under the sun or whatever the hottest thing is we think that is going to help us. But really working backwards, especially in your area, you know, what...

companies might be looking for in LA could be different what they're looking for in Montreal or could be different to what they're looking for in Berlin, maybe because of the industry that's dominating that space or maybe because of the cultural aspects or what, you know,

where technology is heading or trends, local trends, I think there is a lot of benefit to working backwards that way. 100%. There's lots of different websites that give

of what skills in an area. I know LinkedIn has done surveys and I've even talked about those on air recently. I don't know how quickly I'd be able to find. Oh yeah, episode 856. I talked about how AI engineer is the fastest growing job of 2024.

And everything in that episode is from LinkedIn surveys done all across the world. And there's interesting trends like AI researcher is a particularly popular role in San Francisco where there's lots of frontier labs, whereas something like AI consultant is very popular in New York where there's

fewer places that are working at the cutting edge of developing LLMs and more places that are working with clients to make a big impact with those models. And so you're exactly right. There's definitely regional variation. And then also, of course, there's variation by industry. You know, there's like

If you want to be working in the healthcare sector or the finance sector, there's different tools. Maybe if you're working in finance, you need to figure out some way of doing all this stuff on a Windows computer. And Australia is probably more heavy industry-focused, like mining and things like that, and agriculture. Right.

Right, right, right. It really depends. A cool website that one of our members recommended, thank you, Ricky Singh, for recommending this to me recently, is called hiring.cafe. I recommend checking it out. I was surprised at the... It's like an aggregator of jobs. You can filter by location, job titles, and things like that. And yeah, it's kind of very raw type of...

layout, user interface. It's not one of those fancy websites like, I don't know, Indeed or Seek. It's very accessible.

And they do a fantastic job at aggregating all the jobs. So anybody looking for roles or even just to get information on roles in your local area or wherever, like that, the skills are required. I recommend checking out hiring.cafe. Cool. Thanks for that tip. Okay. Moving on. Number four. Story number four is David, an experienced professional.

Who's been in the data science space and they're planning on staying in the data science space. They're in their mid-40s. They have a background in gaming, analytics, and consulting. And they're now staying sharp in data science and not planning to transition to AI. And I specifically like this story because not everybody is going to become an AI engineer. It just shows that you don't have to become an AI engineer.

And the reason I asked David, like, why don't you want to become an engineer? It's the hot thing right now. Everybody seems to want to get into AI. And David explained that

First of all, he's not that technical with, you know, or not just not interesting getting that technical. That's not his passion. He sees at the same time, he doesn't see the role of a data scientist getting replaced by AI because he sees huge value in being the customer facing data science person, basically helping translate insights into business outcomes.

He's interested in using AI, but he's not interested in building AI and fine-tuning and agentic and LLMs and things like that.

Yeah. So what do you think of that? Like, what's, is there value in people staying focused on data science and using AI to their advantage, but not really diving into AI engineering and those other roles that we talked about earlier? For sure. I mean, I think that kind of ties into this idea that I was talking about earlier, where there's like, you know, there's these long-term megatrends that, you know, that you can find relatively solid ground on. And so...

One of those big megatrends is that AI models are going to become better and better and better at being able to help us out on data analytics, data visualization. There's very little reason today why you should be typing out every character in the code that you write. And so you should definitely be leveraging these tools wherever you can.

And then, yeah, the other megatrend is, or the other thing to think about with respect to megatrends is that there are certain spaces where you can find solid long-term ground that don't involve staying up to date on the latest things with LLMs. You can become expert at other aspects of data science. You don't need to be

Yeah, you don't need to be like, wow, everyone now is learning how to call LLMs and stitch them together. Yeah, there's a lot of demand for that, but there will continue to be a lot of demand for data visualization and being able to write performance SQL queries, being able to tell a compelling data story from the results that you have. And so, yeah,

You can focus on those kinds of approaches. You could become expert in Bayesian statistics, which has a lot of applications. That isn't anything to do really with AI or LLMs. And you could be making huge impacts. You could become a huge expert in Bayesian statistics and use LLMs to help you learn that stuff and make it easy to do the work.

But yeah, does that answer your question? Yeah, yeah, yeah. For sure. For sure. And there's always going to be room for that bridge. People connecting the technical insights and takeaways to the non-technical audience. Because

You got to drive, at the end of the day, you got to drive business outcomes. And there's going to be a lot of people like, sometimes I get caught up because I'm listening to this podcast. I'm speaking with our members. I'm like working on like learning things in AI. I'm teaching things and I get caught up. And I think that everybody around me knows AI. I feel like, okay, everybody's on the street. But realistically, it's probably like a small percentage of people in the whole world may be like,

It might be an over estimate to say like 3% of people on the whole planet understand like LLMs and understand even like what a regression is and how classification works and things like that. That's way less than 3%. Yeah, I know. But it feels like that. It feels, you know, it feels to me like probably 30% of people. But I have to, like,

like consciously tell myself it's probably less than three, you know? So you, we get carried, like I personally get carried away. And I think, but then I get woken up from this dream state when I speak to someone just, you know, of my friends, like at a, at a cocktail party or something. And we start talking, I'm like, and they're like, Oh, what, what did you, what do you mean? And then I have to like bring myself back to, Oh, actually, you know, I'm talking to a lawyer who, uh, at this stage is not yet using a genetic AI and things like that. So,

There's always going to be room for people who translate from this small percentage of experts and from this world of tech to the non-tech people, insights, how things work, how things should work, explore their pain points, problems, and things like that. So that's definitely an area of data science that if it interests you, if you feel excited about talking to people and helping people,

then that's a great area to follow. And you definitely don't need to go super technical if you're in that space. A couple of other things that David also said was that

Interesting tip, he's in his mid-40s and he has experienced age bias in recruiting. And what he does is he actively limits what's visible on his LinkedIn to avoid age bias. For example, he's removed his dates of graduation. He's removed, I don't remember what else, like his birthday and things like that. So people and algorithms cannot bias against him in terms of age. You said he's 40? Mid-40s. Mid-40s. Yeah.

Interesting, man.

So that's a tip. Unfortunately, you know, it's sad, but it probably does happen. So, you know, if you want to protect yourself, especially against age bias, whether you're young or old, like whatever you feel is maybe affecting you, that's one thing to go about it. And then once you get into the interview, it doesn't really matter like what age you are. It's all about the skills and it's about the value you can bring to the business. Yeah. You know, there's, I think in data science, there really are people who,

If they're making a transition from some other career, they really are willing to take some big pay cut to get some experience. But it is interesting how I've encountered several times in my career where

Someone else, maybe someone in finance, for example, is coming to mind for me right now. Someone that I worked with in finance. She was in the finance department and was talking about negotiating offers. I said, this person would be coming over from this different career where they were being paid a lot more, but they're willing to take a pay cut to work with us because they love the work that we're doing. They want to have these skills.

And they were skeptical that that's even a thing. They were like, no, it won't work. They won't stay. Everybody wants more money. This is a finance person.

So, yeah, it's just kind of interesting. Like you definitely can, because I think that age bias, I think fundamentally it isn't like, you know, somebody in their mid forties, you're not like, oh, they, you know, they're too old to learn. It's never too old to learn. Like, I think we have somebody in their eighties or something like when the seventies learning in super data science. Nope. No problem at all. That's cool. No, for sure. But I think, I think you get this expectation. Some people in an organization, not me and probably not you, but

But some people get this thing in their head that, you know, people are always, always looking to be making more, to be more senior. And so, you know, it's, it's, it's, yeah, I think that's, that's the thing is that like it, it's the thing about age is that it becomes associated with some, some expectation of some level of compensation, which for some roles that this person might be applying for that you're describing, you know, they might be applying for roles that are like,

So the hiring manager sees it and is like, this person, they've been working way too long. Their salary expectations are going to be too high. I'm not even going to talk to them. Yeah, that's unfortunate. It happens. And there's a tip how you can protect yourself. So finishing up about David, I forgot to mention that he's looking at a salary between $200,000 and $250,000 roles. And also based in

I think mid-US, somewhere in the Midwest, it's called. An interesting trend that he's observed is this back-to-office trend. It's getting more and more traction. And he sees it to his advantage because he is willing to go back to the office. And that, to him, means that there's going to be less competition for roles that he's

in his area from people from all over the world. And he also recommends to look at places

where companies are, because of this back to office trend, places where companies are opening up office. For example, he mentioned IBM struck a big deal, a multi-billion dollar deal with a hospital somewhere in Ohio. And he predicts that there will be growth in terms of AI jobs in that space. And there isn't that much. There is talent, but there is going to be more opportunities there than there is supply of talent.

Yeah, yeah, it makes a huge amount of sense. Great strategy. And yeah, I mean, if you want a fully remote job today, it is increasingly competitive. Why is that happening? Why is this back to office trend happening? So I have this hypothesis that when you are an executive or a manager, you feel powerful.

When you can come in, you know, you come in, imagine you're at like, you know, you're at, you're in New York, you're an executive at Goldman Sachs. You want to come in at 830 and all of your hundreds of underlings have already been slaving away since 730.

When you have guests from your friend from the Sovereign Wealth Fund is visiting New York, you want to be able to take that friend to the office and be able to show them all of your minions. But whereas when you're like, oh, we have a large workforce, everyone's online. Yeah.

You know, it's just, it's so much harder to feel the power of that you have. So I think that's because it's the more senior you are. I read the stat years ago. I'm sure it's still the same. I read the stat like 2022 that the more senior you are, the more likely you are to think,

that people should be back in the office. And that's when I originally hatched this hypothesis. But there is also, I also recently read that younger people, like recent grads, they're also increasingly happy to return to office because you learn so much more. Those are the people that miss out the most, I think. If you're starting off new in a career,

and you're stuck on zoom meetings like you don't you don't develop the same kind of rapport as you do in hallways around the coffee machine uh going out for drinks after work if that kind of stuff is happening all the time um i don't know you just you learn a lot more about your industry and i i think for sure um speaking of uh junior roles i think it's becoming more and more

they're becoming more and more at risk with AI, like agentic AI, automating, let's say, junior lawyer tasks, the whole research of case law and stuff like that, or accountant tasks. I heard of this phenomenon, this theory 10 years ago, but now with this kind of AI, it's becoming even more prominent that

AI is going to automate the junior tasks first and what's going to happen next. Junior people are not going to have an opportunity to train and grow into senior people. And so we're going to have this whole layer or slice of the workforce cut out in certain roles that are easily automated with agentic AI. And then we will face the consequences of that like 10 years down the line where we will have no mid-level people or senior people that would have come from those junior people.

All kinds of things changing, changing quickly. Changing quick, got to keep up, but also like focus on those fundamental, what are they called? Megatrends, as you mentioned.

So you don't keep your sanity. You must keep listening to the podcast. That's a mega trend. The podcast is a mega trend. All right. Number five, Evan. He's an experienced engineer upskilling in ML deployment. So for full disclosure, Evan has decided to move on from super data science and

we, from the membership. And this was part of his exit interview. Oh, really? Yeah. Yeah. Thank you for the transparency. You know, like I, I want to be honest, you know, it's, it's, it's a learning platform. It works for some people. It doesn't work for some people. Evan got, was here for a year. He got everything he wanted to get out of it and moved on. That must, I mean, you can't, it would be kind of wild to expect that like people are members for life in a, something like that. You know, you, you,

I understand that. You know, you want different perspectives. You like, you know, you kind of, yeah, I totally understand that. But anyway, so we had a great catch up. I messaged Evan. I said, hey, you've been one of our, you know, like most vocal and members. I'd love to talk to you and understand, you know, what your goals are and things like that.

And basically, Evan is very experienced. He's based on an island in Europe. I think that's ambiguous enough not to give away the exact location of the person. Based on an island in Europe, but he does have a company, a consulting company, and he works with US clients on machine learning, AI, technology.

software development. So he comes from a software development background, but he has upskilled a lot. So in the past year, he's attended

several of our collaborative workshops. I think he attended two collaborative workshops where he worked in teams of people to build machine learning and AI and deploy them, like models and projects and so on, deploy them. He's also attended a lot of our labs, a lot of our courses, a lot of our mentorship sessions. He's one of those people that we have like mentorship sessions where you select a career path and you then get assigned to a specific mentorship group

based on your career path and you're like the advanced, beginner level, advanced or expert level mentorship group. He actually asked me to put him into all three mentorship groups just so he could interact with all three different of our mentors. Anyway, that was a funny thing about him. So basically,

The interesting thing in his or the pain point that he's facing is that increasingly he is finding that companies where he's working on LLMs or that he's applying for jobs to work on LLMs and agentic AI, they are looking for specifically companies.

cloud skills, production-ready skills, and deployment skills. So he sees that an AI engineer needs to increasingly know how to deploy production-ready systems into cloud-based environments. What are your thoughts on that? Do you think that's a compulsory skill for an AI engineer these days? I don't know about compulsory, but one thing I will say for sure is that

There's huge demand. I've said this on the show many times before. For any kind of software engineering skills, if you learn those alongside data science, AI skills, you broaden by so much the scope of possible jobs that you could get. If you could be straddling the engineering team and the data science team

and figuring out how to make the AI models work for the particular circumstances that have some production use case and some cost-effective performance way for the users, that is valuable. Yeah, for sure. But interestingly, like...

I am hearing more and more. It's been super surprising to me. I'm hearing more and more from our members. We want to, we're learning production ready skills. We're learning deployments. That's why, for example, our collaborative projects are so popular because the final step in the collaborative project, the final phase, which takes like a whole week, is the deployment part.

and more and more members. And, you know, I was speaking with, funnily enough, your friend Ed Donner about this yesterday. We were exchanging emails and he said, um,

as well. He thinks, and I agree with him, that in my view, and he says that the science of AI is supposed to be the important thing, like understanding your data, experimenting with your embedding model, validating the relevance of your context and things like that. But alas, people studying and learning this space more and more, especially at the more advanced level, are looking to deployment skills. In fact, Ed told me he ran a survey

literally last morning to his current students and what's the topic they would like to hear more on. And sure enough, production deployment is in the top spot. So it's kind of like this...

in my mind this because AI engineer is such a like vague term vague role at the moment still shaping up like in my mind from these conversations shaping up in a way that for an like an AI engineer yes there's that abstract level of AI engineer we talked about earlier where you can just use the tools and not really get deeper

But as you get deeper, there are two main areas that you should understand well. The first one is the science of AI. And that's what we talked about, like your embedding model, your experimenting with your data, understanding what model to use. And even to the point of LoRa and things like that, you can go very deep into that AI part.

But also there's a second part, which is the production-ready system deployment and cloud. And that's understanding CI, CD, continuous improvement, continuous deployment pipelines. That's understanding how cloud works, what kind of tools in the cloud you're going to use, understanding whether it's right to use model context protocol like MCP or not, understanding how to build Lambda functions on AWS to deploy

Put your Python code into them, how to use step functions, how to make all of that work together and tie into a system that is working, that can be updated, that can be used by companies. And that's like a whole separate area.

of an AI engineer, like we're talking about like an ideal AI engineer would know both of those things really well and be able to build a great model so that it's efficient and it delivers the business outcome that it needs to deliver and be able to deploy it, monitor it, set it up so that it's running in a cost-effective way. There's security properly implemented

set up that you know it's secure and things like that so I think those two areas if anybody's looking to like build a long term career in AI engineering and really go deep I would really focus on developing skills in both areas and

And am I correct in understanding that you actually have, in superdatascience.com, you have a bootcamp for these kinds of skills coming up? Yes, yes. Now is the time to plug our bootcamp. So basically, because of these things that we're observing and all this demand, we have launched, we've just literally launched a bootcamp. So applications are open. You can go to superdatascience.com slash bootcamp.

and apply there. So it's an eight-week intensive program where you get to work with experts in the field that are actually doing these things. So

people who are an AI scientist and is good at that first area. That's the first like three or four weeks of the bootcamp, understanding that and going deep into that. And then there's also that the last four weeks is the deployment part. You know, we work with AWS,

understanding all those things like Lambda functions and how to structure your deployment, how to do the CI CD and everything else like security and things like that. So you work with an expert in that space. You're in a cohort. They're very small cohorts between

It's going to be between 5 and 10, maybe maximum 12 people per cohort. There's going to be a capstone project at the end. There's projects along the way. There are requirements though. In order to join, you have to have certain minimal level of skills. I just want to be upfront with everybody. You have to have Python foundational skills. We want to make sure that we can move fast in this bootcamp. So you have to know how to work with classes and methods.

how to manipulate data in Python, things like Pandas and NumPy. You do need to have basic cloud skills. So unfortunately, we won't be teaching basics of AWS. You need to know foundational understanding of how cloud works and introductory experience of AWS. But if people need those skills, they can go to cloudwolf.com. Isn't that right, Kiro? Let's not overwhelm people. Yes, you can learn AWS at cloudwolf.com.

And also in superdatascience.com, we teach some introductory level of AWS. So CloudWolf is more focused on certifications for AWS. So at SuperData Science, you can get basic machine learning in the cloud, like things like SageMaker and Bedrock. Also, another requirement is you need to be able to commit 8 to 12 hours per week. That's the bare minimum. So there will be three or four hours in-person sessions, and then you have to still do homework assignments.

And yeah, that's pretty much it. It's always obviously advantageous if you've played around with LLM APIs and some agentic workflows.

RAG and stuff like that, but that's not really required. So those initial things I mentioned are absolute requirements. We're very excited about it. Our first cohort is launching soon. We're aiming to launch it in June. And then from there, we'll be launching the next one. It's probably going to be September, October. So if you're interested, apply. There's a waiting list. You need to put down a $100 deposit. And we interview every single person who applies for the bootcamp to make sure it's the right fit, because if it's not a right fit,

then we want to make sure everybody, we help people and we deliver value. We don't want to waste your time or money or our time for that matter or our space in the bootcamp. As I mentioned, they're limited. So if it's not the right fit, whoever's interviewing you, me or Adlan or maybe someone else, we'll let you know right away that it's not the right fit. And this is the things that you can brush up on in order for it to be the right fit.

Anyway, would love to see people in there if you think this is the right thing for your career. Very cool, Kirill. I would love to take a course like that myself. Me too. Yeah.

I'd never get past the interview though. You guys would be like, this guy is a liability. Overqualified. Well, this has been awesome, Kirill. For full transparency to you listeners who are still listening to this episode all this time later, we were in the middle of, Kirill and I had a business meeting just to talk about the podcast business.

And he was like, can we record a quick five-minute Friday episode? I've got these five people I want to talk about. And now we've been recording for almost an hour and a half. That's crazy. Wow. Yeah, it's going to be a Tuesday episode. And we're probably going to have to have our business meeting next week. But it was fun. It was good fun. I really enjoyed this. I hope listeners enjoyed it too. I felt very...

I felt very relaxed with you. I know you so well. I really enjoyed getting questions from you. I feel like this was a next level back and forth conversation. Yeah, it's the kind of thing. I've never really listened to the all-in podcast very much, but I imagine it's something like that where you have this four hosts on that show and it's always the same people. So you get all that rapport, which I think you and I have. So hopefully people enjoyed this episode. Hopefully.

Kirill, I mean, and that means now this is a Tuesday episode. I have to ask you the usual and the questions. It's, you know, it's probably been, it's been a while since you've been on the show. Yeah. It's been, well, since January. Your last episode was 853. You and Adlen were on the show. Do you have another book recommendation since then by chance? Yeah. Yeah.

Uh, can I just, can I do two, two books I'm listening to? Uh, well, I guess you are one of the owners of the show. So I got it. Okay. All right. Thanks. So, um, in terms of fantasy, I'm loving Joe Abercrombie right now. The, the trial trilogy called the blade itself. Uh, the first law and the first book is called the bell laid itself. I'm about to finish the third book, the reading. You got to listen to it on audible though, because it's read by, uh,

I forgot, Stephen Pacey, I think. And it's incredible. It's kind of like Lord of the Rings, but much funnier. And it's kind of like Game of Thrones. They're like much funnier and bloodier than Game of Thrones. If you can get more bloodier than Game of Thrones. A really funny, really funny book, really fun book. So that's fantasy. And in terms of a self-improvement, listeners who've heard me on the podcast before will know that I'm quite...

excited about growing myself as like my character my personality and discovering my psychology and things like that the most recent um

thing that I've listened to in that space was an amazing podcast on the Tim Ferriss show, episode 798 with Terence Real, who's one of the best relationship psychiatrists or psychologists in the world. And I've really listened to that episode twice now. Why I mentioned it as a book recommendation, because it shares five chapters of his book,

called Fierce Intimacy. I haven't read the book yet. I have just purchased it like two days ago on Audible and I'm going to listen to it. But listening to that episode, 798 on the Tim Ferriss show, really has helped me uncover certain things. And it's about, he talks about that in relationships, you're always in one of three states, which is harmony, disharmony, and repair. And it's all about how you manage repair and what are those childhood experiences

or like inner child reactions that we have. There's five of them and he covers all five and how we go through them and how to notice that and not let yourself fall into those patterns to not ruin your relationship. Because in that repair time,

that's when you can actually grow or you can destroy a relationship. So again, the book's called Fierce Intimacy by Terrence Real. I've only heard five chapters out of it, but so far it's been really transformational for me. I've enjoyed it a lot. I'm really looking forward to reading the full book.

Nice, Kiril. Thank you for those. And then for following you, obviously people can go to superdatascience.com, join the platform. I know that that's the number one place to reach out to you. Yeah, we have a free trial. You can check it out and reach out to me while you're on the free trial.

No problem. Nice. I love that. They probably get extra attention because you're trying to convert them. I wish, yeah. I wish. We have quite a lot of people joining. I think like, yeah, thousands of people joining every day. But of course, I reply as much as I can. Amazing. All right. Fantastic, Kirill. Thank you so much for being on this 5-Minute Friday episode. Thanks a lot, John. We'll catch up with you again soon. See you, man.

Such a fun episode, in it Kirill covered how combining LLM knowledge with machine learning fundamentals can be key to landing an AI engineer role. How our field is evolving faster than individual learning pace, but you can find peace by focusing on long-term megatrends like Python and SQL rather than chasing every new framework. How staying in data science as a business-facing translator of insights can remain invaluable. Not everyone needs to become an AI engineer.

And we talked about how age bias is real, but you can remove your graduation dates from LinkedIn to obfuscate your age a bit. How back-to-office trends create regional opportunities and how in-person networking remains the most effective job search strategy.

All right. As always, you can get all the show notes, including the transcript for this episode, the video recording, any materials mentioned on the show, the URLs for Kirill's social media profiles, as well as my own social media profiles at superdatascience.com slash 899. Thanks to everyone on the Super Data Science podcast team, Kirill himself.

who's the founder of the show, our podcast manager, Sonia Brajevic, our media editor, Mario Pombo, Nathan Daly and Natalie Zheisky on partnerships, our researcher, Serge Massis, and our writer, Dr. Zahra Karche. Thanks to all of them for producing another magnificent episode for us today. For enabling that super team to create this free podcast for you, we're deeply grateful to our sponsors,

You can support the show by checking out our sponsors links, which are in the show notes. And if you're interested yourself in sponsoring an episode, you can find out how at johncron.com slash podcast. Otherwise share, review, subscribe, but most importantly, just keep on tuning in. I'm so grateful to have you listening and hope I can continue to make episodes you love for years and years to come. Until next time, keep on rocking it out there. And I'm looking forward to enjoying another round of the Super Data Science