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 Engineer career path:
Become a AI Engineer
14 milestones 11 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 Engineer from scratch and actually get hired.
Earn on average per year:
136,386
US salary data collected from Indeed, LinkedIn, and Web3.career 2026.
I'll go into more detail around everything you need to know in this guide, but if you want a quick follow-along cheat sheet in the future of what to learn and in what order, then feel free to follow it.
And with that out of the way, let's break this all down.
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 Prompt Engineering
Now that you have your Python foundations, it's time to dive into your first core AI skill, 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:
Just be aware that these free videos are not updated as often as the actual courses.
Learn RAG (Retrieval Augmented Generation)
Now that you understand how to work with LLMs, the next step is learning how to make them actually useful in the real world, and that's where RAG comes in.
The problem with AI models out of the box is that they're limited to what they were trained on. They don't know about your company's internal documents, your latest product data, or anything that happened after their training cutoff. RAG solves this by letting you combine a language model with your own data, so it can retrieve relevant information and generate accurate, context-aware responses.
It's one of the most widely used techniques in production AI systems right now, and it's a skill employers are actively looking for.
And I have a course that covers all of this:
Average time to complete: 32 days
You'll build real projects, including a financial analysis tool, an AI-powered digital waiter, and a RAG system for processing PDFs, all using OpenAI APIs. By the end, you'll also have covered more advanced concepts like multimodal and agentic RAG, which will set you up nicely for later in this roadmap.
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 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:
Believe it or not, you now have the core skills to start for applying for Junior AI Engineer roles, so let's cover how to do that next.
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 those who have all the answers right away!
Step #4. Skill up further
Don't get overwhelmed by the number of skills here. As I said before, you already have enough to get hired. These are just additional skills that can help you be more appealing to employers or even start in more senior roles.
So let's break them down.
Learn to build AI applications with LangChain
At this point you've been working directly with LLM APIs, which is fine for smaller projects. But as applications get more complex, with multiple models, memory, tools, and decision logic, managing all of that yourself becomes messy fast.
That's what LangChain solves.
It gives you a framework for chaining together all the components of an AI application in a structured, maintainable way, while LangGraph takes it a step further, letting you build workflows where the AI can make decisions, loop back on itself, and complete multi-step tasks autonomously.
These are the tools most teams are actually using to ship production AI systems right now.
And good news? We also have a course on this also:
Average time to complete: 25 days
You'll build real projects using the OpenAI and Gemini APIs, including chatbots, a Q&A app for private documents, and a full research agent that pulls from live web data, all using LangChain, LangGraph, and LangSmith.
Once you're comfortable building and chaining AI applications, the next step is learning where to actually find and work with the models that power them.
Learn Hugging Face
Now that you know how to build and chain AI applications, you'll want to start going deeper on the models themselves. Not building them from scratch, but understanding how to find, customise, and deploy the right one for the job.
Hugging Face is almost certainly where you'll find the models you'll use because literally everyone is on there. 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 customise 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.
Not bad right?
Then, once you can work confidently with models directly, the next skill to pick up is building systems that can use them autonomously.
Learn AI Agents
Up until now, everything you've built has been reactive. A user sends a message, the AI responds. But a growing part of AI Engineering is building systems that can work autonomously, taking a goal and figuring out how to complete it across multiple steps without needing someone to hold its hand the whole way through.
That's what AI Agents do. And multi-agent systems, where multiple agents collaborate and hand off tasks to each other, are already being used in production at some of the biggest companies in the world. Getting comfortable with this now puts you well ahead of most candidates.
Here's the course I recommend:
Average time to complete: 15 days
You'll build real multi-agent systems using CrewAI, LangGraph, the OpenAI Agents SDK, and more, including an AI interview coach, a research agent, and a secure agent workflow using Model Context Protocol. By the end you'll have hands-on experience with the tools that are shaping where AI Engineering is heading.
And if you want to go even further, there's one more optional skill worth knowing about.
Learn Data Engineering
This one is more niche, and not every AI Engineer will need it. But if you end up working at a larger company or on more complex AI systems, you'll quickly realise that the quality of your AI is only as good as the data flowing into it. And that's where Data Engineering comes in.
Data Engineers build and maintain the pipelines that move, transform, and store data at scale, the infrastructure that AI systems actually run on. Having even a working knowledge of this puts you in a much stronger position when collaborating with data teams, debugging production issues, or scoping out what's actually possible with a given dataset.
We have a course that covers this also:
Average time to complete: 28 days
You'll learn to build real-time pipelines with Apache Kafka and Flink, create data lakes on AWS, orchestrate workflows with Airflow, and even integrate LLMs directly into data pipelines. It's a broad course that covers the full data engineering stack, taught by someone who has built production systems at AWS and Stripe.
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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