Don't Waste 2026 on the Wrong Career (ML vs AI Engineer)

What's the best move for the coming year? Becoming a machine learning

year? Becoming a machine learning engineer or an AI engineer? Well, I help people land AI engineering roles. And

the first step is always figuring out if that's actually what you want. Because

many people waste hundreds of hours teaching AI engineering and ML engineering at the same time, not realizing these are very different jobs and getting stuck in the process trying to do too much at once. So, in this

video, I'm going to save you that problem by breaking down the real differences so you can make the right decision for yourself. Let's start with what's happening in the market right

now. AI engineers integrate existing

now. AI engineers integrate existing models into applications. They're

building products and tools that solve real problems. For example, you might build a system that stores company information in a vector database so employees can cross reference

confidential data faster. Or you

aggregate a month of customer reviews and then use a large language model to figure out the best next step to improve a product. Here's why AI engineers are

a product. Here's why AI engineers are booming right now. Language models are universal. Almost any problem, any

universal. Almost any problem, any industry, any situation can benefit slightly to greatly from LLM somewhere in the stack. Your job is to integrate these existing models, which don't have to be language models, but it means that

you don't have to actually understand them from a foundational math perspective. You just have to understand

perspective. You just have to understand them functionally. And sure, you might

them functionally. And sure, you might need some linear algebra to, for example, create embeddings for some data, but you're not going to be training models from scratch. In the

most complex cases, you might fine-tune a model or two, but the real focus is in software and data engineering. Your job

is to get the right data in the right place to invoke an AI model effectively and expose that solution to end users safely using good software development practices. Now, machine learning

practices. Now, machine learning engineers play a completely different game. ML engineers more often than not

game. ML engineers more often than not train models from scratch. They need

deep knowledge of math, statistics, and data science. They're dealing with

data science. They're dealing with training pipelines, validation sets, test sets, and yeah, they also need data engineering, but for a different reason.

They need the data in the right place for training and testing, not for production inference. While some

production inference. While some companies expect training from AI engineers and inference skills from ML engineers, this is a clear division that's true for a majority of roles and how you should separate these two roles

in your head. Now, here's the hard truth. While you can self-e a lot in

truth. While you can self-e a lot in technology, ML engineering has brutal competition if you don't have the right academic background because you'll be competing against people with PhDs in statistics and computer science. It's

not impossible, but it's a much steeper climb. Now, AI engineers, on the other

climb. Now, AI engineers, on the other hand, they're essentially software engineers with a new superpower. And

software engineering has been proven to be self-eable by thousands of people in this industry in the past. So you take the same coding skills from traditional software developers and augment them

with the ability to integrate AI models.

Now this is not something that you learn in just a day. But with the right guidance, you can shorten the learning curve significantly. And the day-to-day

curve significantly. And the day-to-day folks is also incredibly different between these two roles. Where an ML engineer is testing for model bias during validation and training, an AI

engineer is running AB tests in production to see if a feature actually improves user experience. So, it's much more about shipping and iterating than it is about theoretical optimization.

Now, if this sounds interesting to you, becoming an AI engineer is probably closer than you think. Let me show you something real quick. This is the AI transcription app that I'm giving to aspiring AI engineers. It runs locally

on our machine, and it's a perfect example of what an AI engineering project actually looks like. So, here's

how it works. You can record your voice and get it transcribed using an AI model. then optionally clean it up with

model. then optionally clean it up with a large language model that removes fill words and unnecessary sentences. Let me

demonstrate. I'll start recording now.

Uh I want to get into AI engineering. So

where do I get started? I'm not sure.

I've been wasting a lot of time trying to find different resources, building relevant projects, but yeah, it's just not really working out. So um I'm not really sure where to get started. Stop

recording. And you can see here we get the original transcript pretty fast. But

it's messy, right? all these filler words, all these o's and you know, so the app uses a local LLM to clean it up.

And here we go. Now it says, I want to get into AI engineering. I'm unsure

where to start, and I've been wasting time trying to find resources, relevant projects. Now, this is much cleaner and

projects. Now, this is much cleaner and actually still keeps the core of what I'm trying to say. This project

demonstrates full stack skills. Browser

APIs for recording, a Python fast API backend, local AI with whisper, and LM integration. And most importantly, it's

integration. And most importantly, it's useful. You can explain this in an

useful. You can explain this in an interview without needing a wall whiteboard. Right? You build a voice

whiteboard. Right? You build a voice transcription tool that cleans up messy recordings using local AI. Done. You can

grab the solution in the link in the description and start building your own version today. Before that, here's a

version today. Before that, here's a final point I want to make about AI engineering. People keep saying software

engineering. People keep saying software developers are in danger, but AI engineers in particular are actually quite future proof. Think about it. If

AI truly becomes as powerful as a lot of these AI boosters claim, you'll still need people who can integrate the models properly, you'll still need engineers who can configure them properly. Whether

that's in Python code, at the infrastructure level, or in the application layer, AI doesn't eliminate the need for engineers. It just changes what we need to build. So, here's what I want you to do right now to make some

real progress. Check out the link in the

real progress. Check out the link in the description so you get access to that transcription app that I just showed you. And if you want a high chance of

you. And if you want a high chance of landing your dream AI job, you should check out my AI engineering community in the description below, where you can learn much faster and actually understand what it takes compared to

wasting potentially thousands of your hours. So, I'll see you there.

hours. So, I'll see you there.