Every app you use, every recommendation you get, every time a system seems to just know what you need... there's an AI Engineer behind it.
It's one of the most sought-after jobs in tech, which is why companies are scrambling to hire people who can actually build AI-powered products, and the gap between demand and available talent is enormous right now.
Even better news?
Getting into this field is easier than you might think. You don't need a PhD, a computer science degree, or years of experience. You just need to learn the right skills, in the right order, and have a portfolio that proves you can use them.
So that's what we're going to cover in this guide!
How to become an AI Engineer without a degree!
Just a quick heads up, but this article is based on our AI and Machine Learning Engineer career path:
Become a AI & Machine Learning Engineer
17 milestones 12 courses
Step-by-step roadmap where you'll learn to code and build a portfolio.
Curated curriculum of courses, workshops, challenges, projects, and action items.
Become a AI & Machine Learning Engineer from scratch and actually get hired.
Earn on average per year:
$195,425
US salary data collected from Indeed, LinkedIn, and Web3.career 2026.
Wait... AI and Machine Learning in the same roadmap?
Yep!
You see, there's a lot of overlap in the skills required for both roles, so you pretty much learn the same thing and then apply it differently:
ML Engineers are more focused on the machine learning models themselves and will even build them from scratch
Whereas AI Engineers cover all aspects of developing an AI application, and usually use a pre-built AI model via an API or similar
But they both use the same tools to some extent, so while you learn one, you can actually set yourself up for getting hired in two in-demand careers. Not bad right!?
So let's get into the actual steps required...
Optional step. Speed up your learning
Because you're going to be learning a lot of new skills, I recommend taking a slight detour and checking out this guide or, better still, this course first:
![Learning to Learn [Efficient Learning]](https://images.ctfassets.net/aq13lwl6616q/6VWcUgLgG0SU55ORlILe2S/e186361aeb48561bcd19ae6486577022/Learning_to_Learn.jpg?w=1200&h=672&q=50&fm=webp&bg=transparent)
Average time to learn: 5.5 hours
It will teach you how to learn using concepts you've never heard of before.
Why care?
Because it'll help you learn faster, which will then reduce the total time it takes you to learn all these other skills you'll need. (It's kind of like stopping the car to fix a flat tire, because you know it will make the whole journey much quicker and smoother).
It's important to understand that AI Engineering isn't typically an entry-level role, so you're going to need a lot of complementary skills. If you can cut just 10% off each, you'll save yourself a lot of time and effort for just a 5-hour detour.
Definitely worth checking out!
Step #1. Learn the core skills
Alright, so time to learn the main skills. 90% of your time will be spent here, but just work through them in order, and you'll be fine.
Learn Python
Although AI Engineers might use Python, Java, or C++, Python is the language that you're most likely to come across in the field, which is why I recommend learning it first:
Average time to learn: 60 days
Python is very beginner-friendly, so even if you pick up the others later on, you can start here and get moving, and then come back to more languages when needed. However, don't worry about those for now, as we have more important skills to pick up first!
Learn the core concepts around AI Engineering and Machine Learning
You know how a mechanic doesn't just know how to drive a car, but they also understand what's happening inside the engine? That's the difference between someone who uses AI tools and someone who builds with them.
As an AI Engineer, you need to understand how models actually learn from data, why certain approaches work better for certain problems, and what's really going on when something breaks. That understanding is what lets you build things that work reliably, not just in a demo but in production.
So what does that actually look like in practice?
Well, you'll need to learn the mechanics of how machine learning models are trained, how to work with data to get useful outputs, and the core subfields that power most of what you'll be building.
What like?
Natural language processing for anything text or language-based, computer vision for anything image or video-based, and deep learning, which is the neural network architecture sitting underneath most modern AI.
You'll also need to pick up the basics of tools that the industry actually uses day to day, like TensorFlow and PyTorch, so you're not just learning concepts in theory but applying them to real projects from the start. (With a deep dive into them more later on).
The good news is I have a course that covers all of this:
Average time to learn: 45 days
Better still, I've also gone ahead and shared the first 10 hours of the course below for free:
Just be aware that the course is updated regularly each year, whereas this video is a few years old now.
Learn Prompt Engineering
Just because you're going to be working behind the scenes with AI models doesn't mean you can't take advantage of one of the fastest-growing skills in 2026, and learn some Prompt Engineering:
Average time to complete: 24 days
This program will help you learn how LLMs actually process instructions, why they sometimes ignore what you ask, and the specific techniques that get predictable results.
You can check out the first 5 hours of this course for free below:
Again, though, just be aware that these free videos are not updated as often as the actual courses.
Learn Hugging Face
At some point in your AI Engineering career, you're going to need to work with a pre-trained model. Adapting them to specific tasks, training them on your own data, and deploying them somewhere useful.
Hugging Face is almost certainly where you'll find the models you'll use because literally everyone is on there. In fact, companies such as OpenAI, Google, and Apple all share their open-source models there, while individuals can create their own profiles and begin building their ML/AI portfolios, so it's definitely worth learning how to use.
And the good news is, I have a course on it:
Average time to complete: 28 days
I'll show you how to customize real machine learning models with Hugging Face as the base, including text classification models, object detection models, large (and small) language models, vision language models, and more.
You'll also get hands-on experience with the Transformers library, which is the backbone of most modern AI workflows, and learn how to share and collaborate on models through the Hugging Face Hub.
Learn Pytorch
Once you understand how machine learning models work conceptually, the next step is learning how to actually build and train them. And PyTorch is the framework most AI Engineers use to do that.
Why?
Simply because a lot of the latest AI research is published using PyTorch code, which means when new techniques, model architectures, or new training approaches come out, they almost always show up in PyTorch first.
And a big part of working in tech and AI is staying on top of these changes:
Average time to complete: 50 days
Better still? I'll also show you how to build and train deep learning models from scratch!
Learn TensorFlow
Not every company uses the same tools, so it's worth learning the other big hitter in AI and ML, which is TensorFlow. It's used by companies like Google, Airbnb, Uber, DeepMind, Intel, and IBM.
And the good news is I have a course on this too:
Average time to complete: 60 days
Now, although you'll learn similar skills to what you covered in the PyTorch training, it's definitely worth learning this tool as it has more of a production focus, giving you experience deploying models at scale, which is a different and very valuable skill set.
Not only that, but you'll also get hands-on experience building three real projects across three completely different types of AI problems: computer vision, natural language processing, and time series forecasting. Always handy when it comes to interviews!
Learn how to deploy models with SageMaker
Building a model that works on your laptop is one thing. But getting it to run reliably in a real product, handling thousands of users, scaling automatically, and not falling over under pressure is a completely different challenge.
And that's exactly what SageMaker solves.
It's one of the most widely used tools in the industry for training, fine-tuning, and deploying machine learning models at scale, and the good news is I have a course on it also!
Average time to complete: 28 days
You'll build end-to-end projects using real datasets, Hugging Face models, and PyTorch, which are all things you've already learned earlier in this roadmap.
But most importantly, you'll then take the next step and deploy those models to production with AWS, writing your own training scripts, monitoring jobs, deploying scalable endpoints, and stress-testing your models with real production traffic.
And good news? You can check out the first 4 hours of this course in the video below for free:
Once you know how to deploy models, though, there's one more skill that puts you in a completely different bracket as a candidate...
Learn how to fine-tune LLMs with QLoRA, AWS, and Open Source
Most AI Engineers know how to use existing models, but the ones who can take an open-source LLM and customize it on proprietary data for a specific business use case? That's a much smaller, much more valuable group.
Now, you'll have already learned fine-tuning techniques with Sagemaker. However, this course teaches you how to do it using QLoRA, which is a technique that dramatically cuts the compute resources required.
Why care?
Simply because this makes it far more practical to run on real business infrastructure.
Average time to complete: 28 days
You'll learn how to preprocess proprietary datasets, run fine-tuning scripts, monitor training jobs in CloudWatch, and deploy your customized LLM using AWS SageMaker and Streamlit. So that by the end, you'll have a production-ready, fine-tuned model you can add straight to your portfolio.
Speaking of which...
Step #2. Build a portfolio to prove you can do the work
Want to know something cool?
Tech companies don't really care about your qualifications. What they actually care about is whether you can do the work. This does mean, though, that you have to prove it via in-depth interviews and by having a portfolio of your work.
The good news is that fellow ZTM instructor Dan Schifano goes through each of the tasks to set up a portfolio in his course on personal branding, as well as some other great tips to help you stand out even further.
Average time to learn: 1-2 weeks.
Once you've built your portfolio, make sure to go ahead and add all your projects to it that you've built so far during the course of this roadmap. Especially if you've tweaked any for your own personal changes.
It's ok if you haven't, but definitely give it a try. Building something yourself is what gets you specific knowledge, the type of knowledge you can't get from courses.
Step #3. Apply for AI Engineer jobs
Alright, now it's time to apply for jobs and get hired! However, if this is your first-ever tech job, you need to understand that the hiring and interview process at tech companies is different from those you might have worked before.
It can vary based on the company, but the process is usually:
An online quiz when you first apply (although not everyone will do this). It’s a simple filter to see if you’re worth spending one-on-one time with, as they get thousands of applicants
A basic technical assessment done from home
An in-person technical interview where they ask you how to solve specific AI-focused questions
Potentially a final project to complete that would replicate your daily work. This will give them an idea of how you work, as they want to hire people who are not only capable but can deliver on time
A behavioral interview to see if you would make a good team fit
Yes, it's more effort than a normal job, but for 6-figures in salary and no degree required, I think you can agree it's worth the effort and money saved!
So, how do you pass the interview?
Well, if you're a ZTM member, then I HIGHLY recommend you check out Andrei's course on getting hired at your dream job:
Average time to learn: 12 days.
He covers the entire application and interview process in detail, including his technique, where he gets a 90% interview success rate!
I will also say that workflows often matter more than immediate correct solutions, so if you don’t know something, talk through how you would go about trying to solve it. This is because interviewers are often looking for people who show the ability to figure it out, not who have all the answers right away!
What are you waiting for? Become an AI Engineer today!
So there you have it, the entire roadmap to becoming an AI Engineer in less than 12 months.
Is it a lot to learn? Yes. But all it takes is just one step at a time.
All you have to do now is simply start learning and follow the path. Time will pass either way, but your career could look very different if you set your mind to it!
P.S.
Want some great news?
All of the courses I’ve mentioned in this guide are included in a single Zero To Mastery Academy membership. That means if you become a member, you have access to all of these courses right away and will have everything you need in one place.
Plus, as part of your membership, you'll get to join me and 1,000s of other people (some who are alumni mentors and others who are taking the same courses that you will be) in the ZTM Discord.
Ask questions, help others, or just network with other AI Engineers and tech professionals.
Make today the day you took a chance on YOU. There's no reason why you couldn't be applying for AI Engineering jobs in just a few months from now if you follow the steps I outlined and put in the hard work.
So what are you waiting for 😀?
Come join me and get started on becoming an AI Engineer today!
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