How NOT to Become an AI Engineer

AI engineering is one of the best career opportunities in tech right now. Most of you watching this already know that, but here's what I keep seeing. People are spending months, sometimes over a year, grinding through courses or tutorials and they still feel like they're not getting anywhere. And the reason isn't effort, it's the direction. They're doing the wrong things in the wrong order and nobody's told them. So, in this video, I'm going to walk you through seven mistakes that I see people make all the time when trying to become an AI engineer and what you should actually be doing instead. But before we jump into that, I do want to start by explaining what an AI engineer actually is because this is where a lot of the confusion comes from. It also directly ties to a lot of these mistakes that I'm going to go over. Okay, so what is an AI engineer? Well, when people hear AI engineer, they picture someone training massive models from scratch, writing research papers, doing heavy math. Now that's a machine learning engineer or maybe a researcher potentially maybe data scientist but that's a different role and also a different skill set. An AI engineer builds applications on top of pre-trained foundation models. So things like GPT, Claw, Gemini, Llama, etc. These models already exist and they're incredibly powerful and your job as an AI engineer is to take them and build real products. So chat bots, search systems, AI agents, automation tools, rag pipelines, right? These are the things that actual users and businesses interact with. This is a hands-on practical role. You're not publishing papers. You are shipping code. And that distinction matters a lot because it completely changes how you should be spending your time in approaching getting into this type of role. So with that said, let's get into the mistakes. So mistake number one is spending too much time on theory. Now this is probably the most common mistake because someone will decide that they want to get into AI engineering. Then the first thing that they do is they sign up for courses on calculus, linear algebra, probability, you know, traditional ML theory. And usually they end up spending months working through mathematical proofs before they ever write a single line of application code. Now look, I'm not saying that you don't need any of this. Yes, it is useful to know this, but you really just need the fundamentals. What are embeddings, right? How do you evaluate a model? What's the difference between supervised and unsupervised learning? How does a neural network work? Right? What are the different kind of basic architectures there? This is a conceptual understanding. That's what matters. But you don't need to know all of the details because realistically, nobody's going to be hiring you to write loss functions from scratch or prove why a certain optimization converges, right? That's research territory and that's a completely different role. Your job is to take the models that already exist and build products that people actually use. And this is the frustrating part is that most courses don't tell you that. They have you grinding through all this random stuff for months and by the time you're done, you can explain attention mechanisms on a whiteboard, but you've never actually learned how to wire up an API call or write a serious piece of software. So, here's what I would rather see. Instead of spending months learning all of the math behind the transformer architecture, spend a weekend reading just enough so you have a conceptual understanding and then start building something. Make mistakes, work on projects. You're going to learn much more doing that. So, make sure you're being very practical in this role when you're trying to get into it because that is what the role is, practical engineering. It's AI engineering, not research engineering. Now, we move on to mistake number two, which is ignoring software engineering fundamentals. Now, it's funny because this is kind of the opposite of mistake one. And to be honest, it's just as bad. Now, people skip a lot of the heavy theory, which is fine, and they jump straight into AI, but they also skip learning how to write good software and just basic code. Now they can hack together a script that you know calls an API but the code is messy. There's no tasks. There's no version control. There's no structure. When it comes to deploying it, they are completely screwed. The reason for this is that they didn't learn the fundamentals of software engineer. Because here's the thing, AI engineering is really just a specialization within software engineering. It's really not a completely different discipline. And if you want to be a good AI engineer, most of your job is going to be traditional software engineering work that just happens to at some point interact with some kind of AI model. Really, AI engineering is just building software that happens to include AI. So if you think that I'm going to become an AI engineer and it's going to change my life and I'm not going to have to do all the software engineering stuff, that is completely wrong. You need to set up APIs, backend services, you need to write tests, you need to do code reviews, you need to manage deployments. All of these things are absolutely fundamental and if you can't write clean Python code, if you don't know Git, if you've never really structured a real project, you're going to hit a wall and you're not going to be able to create something useful. So, make sure that you master the fundamentals. Know your Python, know your Git, know your project structures, be able to write readable code, you know, understand APIs, the things that you would learn in a basic software engineering course. You need to know that. Now, mistake number three is tutorial hell. Now, I talk a lot about this on this channel, and even though majority of what I make here is tutorials, I don't want you to get stuck in tutorial hell. Now, tutorial hell is especially dangerous in AI engineering because this field moves so fast, and there's always something new that you could be watching, right? I'm sure you guys have seen already, oh, the new codeex model, oh, the new Opus model

and there's a million YouTube videos that you feel like you need to watch to keep up. But the thing with watching videos is that you can watch someone like me go through and build a rag agent or a chatbot or, you know, deploy an ML model and you'll feel like you're learning something and that you know what you're doing, but then as soon as you actually have to do it yourself, you're completely lost. Now, that's the trap of tutorial hell is that when you're watching someone else code, especially in a perfect edited environment, it feels like, yeah, super easy. I can figure that out. And then you sit down to do it and again, you just can't get it done. So, make sure that if you are going to consume content that you're consuming maybe 20% of the time and the other 80% of the time you're actually writing code, building, working on your own, making mistakes. Watch a tutorial and then go and try to replicate it yourself and spend five times the amount of tutorial doing that. Now, this is a simple concept, but honestly, it's not easy to do. And if you do follow this, I guarantee you're going to learn so much more. Now, very similar to that, another big mistake that you might be making is that when you are watching content, you're passively consuming it. Now, studies have shown that when you just watch tutorials or read articles, you only retain about 20% of the information. But with active learning, where you're actually coding, building, right? Being evaluated, the retention can jump from up to 75 to 90%. Now, that's a massive difference. And to be honest, that's why a lot of people get stuck. Now, this is exactly why I recommend Data Camp. Their whole platform is built around interactive learning. You're writing real code from day one, and you're not just watching someone else do it. Now, Data Camp is sponsoring this video and I want to tell you about their associate AI engineer for developers track because it's the opposite of everything that I just warned you about in this video that I'm going to go through in the future. Now, I've been working with Data Camp for years now. I've also used it myself, so I'm confident recommending it. Now, instead of jumping between random tutorials, this track gives you a structured 26-hour path where you build actual AI applications. So chatbots, semantic search engines and recommendation systems using tools like open AI API, hugging face, lang chain and pine cone. Now it also covers LLM ops. So deploying models, handling rate limiting, you know, structuring outputs, the production skills that most people skip entirely. And when you're done, you can take their AI engineer for developers associate certification, which includes a timed theory exam and a 4-hour practical where you build a real AI app. Now, that's something concrete that you can show employers that's recognized in the industry. Now, right now, you can get 25% off Data Camp using my link in the description. So, if you want to avoid these mistakes, check it out. It's great. Again, I've used it myself. Link below. So, now we move to mistake number four, which is learning tools instead of concepts. Now, this is subtle, but it does trip up a lot of people. Now, you spend weeks, maybe months, becoming an expert in one specific framework. So, let's say maybe Langchain. Now, you memorize the API. you know every chain type, you can set up agent workflows blindfolded and then a breaking update drops or a new framework comes out that everybody's switching to and suddenly a bunch of what you learned seems completely irrelevant. Now, if this sounds familiar, it's because a lot of you focus on the tool, not on the concept. The AI tooling ecosystem moves super fast. There's new libraries, new frameworks, new model providers. Something drops every single week. Now, if your knowledge is tied to a specific tools API, you're really just on a treadmill here where you need to keep learning the next tools. What you want to focus on instead is the underlying concepts. So, if you're learning something like lang chain, don't just memorize the syntax. You want to understand what the chains and agents are actually doing under the hood. Learn something like the architectural pattern behind retrieval augmented generation, not just how you set it up in one specific framework. understand what vector embeddings actually represent and how similarity search works at a conceptual level. Now, when you understand the concepts, you can pick up any tool extremely quickly. When you only learn the tool, you're starting from scratch every single time. So, the frameworks are really just implementation of these various details and the concepts are what makes you an actual engineer. Now, mistake number five is trying to learn everything at once. Now, AI engineering has an enormous surface area. prompt engineering, rag, agents, fine-tuning, multimodel, embeddings, vector databases, LM ops, evaluation, MCP, right? You know, we can go through forever. The point is that if you're trying to learn five new things at the same time, you're never going to learn one effectively. So, what might happen here is you spend a week learning rag, then you jump to agents because that seems more important. Then you hear about fine-tuning and you switch to that, then multimodal, then something else and a few months in and you're just a beginner at all of these different concepts because you didn't focus on one and actually learn it deeply. I always say this, if you want to be hirable in this industry, you need to be good in one particular area. Now, of course, there's a lot of these things that you should know and you should learn, but learn them one at a time. Understand them deeply before you move on to the next concept and don't just jump to the next AI framework the second that it comes out purely because it's the new hype thing that everybody's talking about. I have a friend of mine who's an AI engineer. He only does computer vision. That's his pure focus and he's makes massive amounts of money. I think he makes like 100k a month selling AI agents and building AI software for companies because he's really good at computer vision and they want to hire

him to do that. So the point is go deep before you go super wide. Pick one area, learn it properly, build something with it and then if you want to move to something else, move on. Okay? So just keep that in mind and make sure you're getting that depth over just the pure breadth. You want to learn a concept thoroughly and have kind of those T-shaped skills where you have that surface area but then you have depth in one area. Now, mistake number six is never deploying anything. Now, I know so many developers who have tons of projects sitting on their machines that nobody can actually use. Everything lives in a Jupyter notebook or runs on local host and they don't realize how much they're missing out on by doing this. Now, here's why this matters. In the real world, AI engineering is about building things that actual people use. And the second that you deploy something, you run into a whole category of problems that you just never see locally. Latency, for example, your AI features take forever to respond. Cost, your prototype works great, but now it's running through hundreds, thousands, tens of thousands of people, and you are going broke. Reliability, your system works maybe 90% of the time, but the rest of the time it's completely broken. These are problems that separate someone who's done tutorials from someone who can actually do the job that people are hiring hiring, sorry, AI engineers for. And the only way to encounter these problems is to actually deploy applications. Now, you don't need anything fancy. You can literally just deploy a simple fast API app to a cloud provider. You can use Verscell. You can have five people using it. It doesn't matter. The point is you need to actually put this out in the wild and deploy it and learn those skills. Now, the last mistake that I see a lot of people making is not building things that solve real problems. Now, if your portfolio is full of, you know, my chatbot and my rag demo and, you know, an agent that summarizes articles, you just look like everybody else. These are the AI engineering equivalent of doing like a to-do list or a weather app, right? They show that you can follow a tutorial, but that you don't actually know how to think. So, the projects that actually stand out are the ones that solve real problems. Build an AI tool that helps with the workflow of your current job. Automate something tedious in an industry that you understand. Solve a problem that you personally have and ship it so that other people can use it as well. This doesn't need to be groundbreaking. In fact, it's actually usually better if it's simple. It just needs to be real. It needs to be something where if you were to describe it to someone, they go, "Oh, that makes sense. " You actually solved a real problem. That's what we need at this company. Come and solve real problems for us. Don't just build weather apps or the equivalent in AI engineering. Now, when you do this, you think about the full picture. So, what problem are you solving? Who is it for? How does it handle the edge cases? These are the types of questions that are going to come up in interviews that people are going to ask you and that you're going to need to solve on the real job. All right, so quick recap here. The seven mistakes that I had is spending too long on theory, ignoring software engineering fundamentals, getting stuck in tutorial hell, learning tools instead of concepts, trying to learn everything all at once, never deploying anything, and building projects that never solve real problems. Now, if you're making some of these mistakes, don't worry about it. It happens. I've made many of them myself. The important thing is to recognize it and adjust it, right? AI engineering is an incredible opportunity right now and it's only growing in pace, but it's a practical field and the people that succeed are the ones who build real things, not the ones who consume the most amount of content. Anyways guys, I hope you enjoyed the video. If you did, make sure to leave a like, subscribe, and I will see you in the next one.