Learn AI Engineering Like a GENIUS and Not Waste Time

0:00 Everyone is trying to become an AI engineer right now, but most people are learning the wrong things. They're collecting courses, frameworks, and certifications without understanding what companies are actually hiring someone to build. If you're new here, my name is Gene Lee. I've been working in

0:17 tech for more than 20 years, and I've hired hundreds of engineers at companies like Meta. The candidates who get hired the fastest are rarely the ones with the longest list on their resume. So, I'm going to show you what an AI engineer actually does, what skills employers are looking for, and the fastest path from

0:35 learning to getting hired. Because the real question is not how much AI do you really know. If you've been told your entire life that safe path is to get a degree, you need to understand how the world has changed. In the age of AI, degrees and certificates don't carry the weight they used to. Anyone can say they

0:54 know Python. Anyone can follow a tutorial and vibe code a project together on a weekend. When I was hiring engineers at Meta, I never looked for claims. I looked for proof. Let me explain. Is this person going to actually be a helpful member on the team? So before you even start to build

1:11 proof, you need to first understand what you're building proof of, which means we need to first understand what the job is. People mix up AI and machine learning engineering all the time, and that can send you down the wrong path. A machine learning engineer builds and train models. You need to understand

1:27 statistics, understand models and data pipelines. An AI engineer is much closer to a software engineer. You're not training the model from scratch, but you're taking a model that's already built like OpenAI or Google's or Meta, whoever's, and using it to build a real product. You're building and connecting

1:46 systems and shipping products. That is a new role. is growing fast and it pays really well. The average salary is about $390,000 a year at top companies. And this isn't a research track, it's an engineering track where you're expected to ship products. Okay. So, if that's the job, what does it actually take to

2:06 get hired for it? There's the option of starting with the college syllabus like linear algebra, calculus, statistics, and that is what you learn to get a degree. It's not exactly what you do to get higher though. And instead of just guessing, I pulled 140 real job postings like AI engineer, applied AI engineer,

2:25 gen AI engineer, and broke them down line by line. One job posting can be misleading. It might just be a hiring manager's wish list for a unicorn that doesn't exist. But when you look at 140, it will tell you a story. And the pattern was very consistent. Top three skills that were mentioned over and over

2:45 were Python, AWS, and hands-on experience with AI agents. And that's not just my opinion. That's the market telling you in their own words what is willing to pay for. Everything that only showed up once or twice does not matter as much. I put the full breakdown for AI engineering plus 18 of the hottest jobs

3:06 in tech right now on my subsack. So, if you want the raw data, I'll put the link in the description. Now you know what the market wants. So, how do you actually get good at that? Well, not by mastering a checklist, but by building the right projects the right way. Everyone's always saying just build

3:22 projects. But what does that even mean? That doesn't actually tell you what to do and how to do it. So, here's what I would do instead. You're going to build three projects in order. Project one, you are going to follow a tutorial. Your goal here is to understand the mechanics and the foundation. And for project two,

3:37 you're going to start building something similar, but change the goal a little bit. You're going to swap the data set, maybe add a feature that nobody showed you how to build, or change something on your own, and you come up with your own ideas to do this. And that's why I partner with Data Camp for this video.

3:53 Because in my 20 years in tech and having hired hundreds of engineers, one thing I've seen constantly is that candidates who do get hired the fastest are not the ones who watch the most content. They're the ones who built the most things. And AI engineering is not a spectator sport. Most people actually

4:12 underestimate how big the differences between passive and active learning. There are studies that show that you only retain a fraction of what you learn when you're doing it passively. But when you're actively writing code and learning hands-on through real exercises, retention can jump closer to

4:30 90%. Data camp is designed with exactly that model in mind. short lessons, then you immediately write real code in the browser and get feedback right away. If you're serious about learning AI engineering fast, their associate AI engineer for developer track is where I would point you to. It's about 26 hours

4:49 across nine courses and last refreshed in May, so the content is very current and it teaches the exact stack you need to build real AI apps. things like open AI API, hugging face, lang chain embeddings and pine cone plus prompt engineering and LLM ops so your projects can stay reliable, not just cool demos.

5:10 This is the structured path that gets you through the first two projects of the three project framework. And if you're earlier in your journey, their AI fundamentals track is a great no code first step to understanding LLM's Gen AI and machine learning before you touch the tooling. Links to both tracks are at

5:28 the top of the description. Now, let's get back to the three project rule. So, project one and two get you moving. Project three is the one that actually counts, which is to build something from scratch that solves a real problem you actually have. This time, no tutorial from start to end. It's all on your own.

5:47 You need to do step one and two to get to step three. Because if you just jump at step three, it might be very overwhelming. You might not know where to start. And the thing about those three projects is that they weren't really teaching you Python or AWS or whatever tool that you picked. They were

6:05 teaching you something else. People see skills list like Python and agents and think I need to master all of them and then I'm done. Right? So you go hunting for the perfect course for each one one by one. But a road map is not as helpful if you don't know how to learn them. And the thing about working in tech is that

6:23 the framework that is popular today might get replaced completely next year. I actually ran this analysis last year and the list was different. Python was still on top same as this year. It also had cloud, genai and C++. Now C++ is gone from the list today and AI agents are in. I'm not telling you that you

6:45 should go learn AI agents and never learn C++. But the point is that nobody knows what's coming next. And the actual skill companies are hiring for is the ability to pick up and learn a new tool really fast and use it to solve real problems. So your goal is not to check off all the stacks, but you should be

7:06 able to learn any new stack and figure things out on the job. I like to compare this to surfing. If you've ever done surfing or been to the beach, you would know that you don't actually control the wave. You learn to read the wave and move with whatever it throws at you. And that is the actual skill to survive in

7:25 tech. And if you did those three projects, that's exactly what you're training for. And when you get to project three, that's where most people give up. And that's because it's the moment you start putting your own work out there for someone else to use and you're building your own ideas without

7:42 anyone holding your hands. Your code will be ugly. You're going to find bugs and you might not know how to figure it out. And there is a voice that shows up right around here that says, "Maybe I'm not ready yet." But you're going to ignore it and push through anyways because the goal was never to be

7:58 perfect. The goal is to make the product work, fix those bugs, and chase down those errors because that is the workout. That practice is what builds the muscle of being able to deliver and build and ship products. And I keep saying, let real people use your products because the types of bugs and

8:15 the reports that you're getting from real people are the real experience. It's not something that's confined to a tutorial. It's the messy version from the real world. Now, let's talk about the fear underneath all of this. If AI is solving the hard technical problems, what's actually left for us to do? Well,

8:33 the value of work is shifting. It's moving away from do you know how to call an API and towards do you know which problem is even worth solving using the API. It's about knowing where AI will be useful versus where it will be unreliable. You need to be able to figure out what to build next. And when

8:52 I was managing engineers, this was the clearest line I saw between junior and senior engineers. Junior engineers needed to be told exactly what to build next step by step. Whereas senior engineers got handed a goal and they figured out the whole path themselves. And that's the judgment part, right? And

9:09 the good news is that this is learnable. You can build judgment in two ways. One is by building things and two is by taking apart other people's things. So every time you see a new AI product or feature, you want to start breaking it down. Train yourself to think about what user problem this product is actually

9:28 solving. Will AI be useful to solve this? What are the obvious way it could fail? And do it enough times and you start developing an instinct. And that is the job. Not collecting tools, but learning how to learn and knowing when to stop. I also had a great conversation with an executive at GitHub about

9:48 building judgment in the age of AI and he's been hiring engineers for even longer than me. So, I will see you in that video right