AI is everywhere nowadays, helping to power the tools we use and the apps we rely on. But while everyone is talking about it, very few people actually know how to build with it.
That’s where AI Developers come in!
It's one of the most exciting new roles in 2026, but the question, of course, is how do you actually become an AI Developer?
In this guide, I’ll give you a step-by-step breakdown of the exact skills to learn along with the resources to learn them, the tools to master, and the projects to build, so you can get hired, earn great money, and future-proof your career.
Let’s get into it.
How to become an AI Developer without a degree
Just a quick heads up. This guide is based on our AI Developer roadmap:
Become a AI Developer
20 milestones 17 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 Developer from scratch and actually get hired.
Earn on average per year:
$155,257
US salary data collected from Indeed, LinkedIn, and Web3.career 2026.
Feel free to use the roadmap in the future as a quick cheatsheet to follow along with, but I'll go into far more detail in this guide now.
With that out of the way, let's get into all these steps.
Optional step. Speed up your learning
Because you're going to be learning a lot of new skills to become an AI Developer, I recommend taking a slight detour and checking out this guide and this course:
![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 complete: 5 days
This course 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).
Like I say, it’s optional but definitely worth it.
Step #1. Learn the core required skills
Learn Prompt Engineering
One of the key parts of becoming an AI Developer is learning how AI works under the hood and how to prompt it effectively, particularly Large Language Models.
Average time to complete: 24 days
You can check out the first 5 hours of this course below for free below:
Just be aware that these free videos are not updated as often as the actual courses.
Learn Python
Now that you’ve learned how AI works and how to prompt it, you still need to pick up a programming language for the development side of being an AI Developer.
Average time to complete: 60 days
Why learn a language if you're just going to use AI? Because like any tool, you can't use AI to help you speed up your work until you understand what it is you're trying to do.
So you need a language, and I recommend Python for a few reasons:
It’s relatively easy to learn
It can work for full-stack development
It’s widely used - especially in AI
You can check out the first 8 hours of this course also for free below:
Learn vibe coding
The problem that most people have with vibe coding is that they try to get AI tools to build things, with no prior experience. That's why there are all these apps with security issues and broken features out there.
But because you now understand prompting and have a programming language under your belt, you can at the perfect point to combine these skills and learn 'vibe' coding the right way:
Average time to complete: 20 days
This will help you understand the right mindset and workflow for directing these tools to build professional projects, giving you a significant advantage in any modern tech career.
You’ll also learn how to prompt effectively, using coding-specific tools like Copilot, Cursor, and Gemini to build portfolio projects that you can be proud of. By the end, you’ll even have a professional portfolio site showcasing your work and the know-how to speak about it confidently in any job interview.
Speaking of portfolios...
Step #2. Build a portfolio to prove you can do the work
Another slight detour, but 100% required.
So, the really cool thing about working in tech is you don't need a degree to get hired. However, you do need to prove you can do the work, and that’s where portfolios come in. You set one up and share your work there so that prospective employers can see what you’ve done and then possibly hire you.
Now technically, you'll learn how to build a portfolio site as part of the Vibe coding course in the last step. However, you can also follow this course as it has additional tips on how to make yourself far more hireable:
Average time to complete: 10 days
Once you've built your portfolio, make sure to go ahead and add in any projects you've built so far!
Step #3. Apply for AI Developer jobs
By this point, you know enough to start applying for Junior AI Developer jobs.
The tech interview process is a little more indepth and longer than most other jobs you will have applied for before. Which is why I recommend checking out this guide and this guide, as well as Andrei's course on getting hired at your dream job:
Average time to complete: 12 days.
He covers the entire application and interview process in detail, including his technique, where he gets a 90% interview success rate!
That being said, it can take a few months of applications to get a job locked in, so let's look at some further skills you can learn...
Step #4. Skill up further
To be clear, you don’t need to learn all of the skills I'm about to list. I’ve just included them here as an example of different areas you can drill down into.
The goal isn't to know everything about the industry, but to start layering in more specialized skills that make you more effective, more versatile, and frankly, harder to replace.
I would recommend looking at the companies you want to work at and the skills they are looking for in more senior roles, and then adding the required skills below.
With that out of the way, let's take a look at a few options.
Build smarter apps with RAG (Retrieval-Augmented Generation)
Language models are powerful but they only know what they were trained on. So if you want your AI app to answer real-time questions using up-to-date or private data, you need Retrieval-Augmented Generation (RAG).
Average time to complete: 32 days
This course teaches you how to combine LLMs with external data sources like PDFs, documents, and databases so your apps can generate better, more accurate responses.
You’ll learn how to use the OpenAI API, implement fast search with FAISS, and build real-world projects like chatbots, AI financial analysts, and data-driven customer support tools. It even goes beyond the basics with hands-on projects using multimodal RAG and agentic workflows so you’re not just building one-off tools, but AI systems that actually understand context and handle complex tasks.
Build AI agents with AWS
AI agents are all the rage nowadays. And in this course, you'll learn how to design intelligent systems where multiple AI agents work together to complete real-world tasks.
Average time to complete: 6 days
Why does this matter?
Because most real-world AI tools don’t run in isolation - they work together. And so learning how to orchestrate AI agents across cloud services gives you a major edge when it comes to building apps that go beyond single prompts.
This is a practical, hands-on way to learn how to design intelligent automation, which is something that’s already becoming a key part of enterprise AI systems.
Learn how to deploy models with SageMaker
At this point, you should have already be working with ChatGPT, Gemini, or other LLM APIs. So now you're ready to go from using models to actually building and deploying them:
Average time to complete: 28 days
This course teaches you how to use AWS SageMaker, which is one of the most widely used tools for training, fine-tuning, and deploying machine learning models at scale.
You’ll build end-to-end projects using real datasets, Hugging Face models, and PyTorch and then deploy your models to production with AWS, so that by the end, you’ll understand the inner workings of LLMs, from tokenization to transformers. You’ll also write your own model training scripts, monitor your jobs in CloudWatch, deploy scalable endpoints, and stress-test your models with production traffic.
You can check out the first 4 hours of this course in the video below for free:
Use the OpenAI API to build intelligent apps with GPT, Whisper, and DALL·E
This course is perfect if you want to go from copy-pasting prompt snippets to building real-world, multi-modal AI applications with OpenAI’s ecosystem.
Average time to complete: 2 days
You’ll learn how to use the full suite of OpenAI APIs, as well as Whisper for speech-to-text, DALL·E for image generation, and text-to-speech for voice interfaces. These tools are the foundation of many of today’s most popular AI products.
But this isn’t just a sandbox. You’ll also build a full AI-powered daily meal planner that understands user preferences, generates healthy recipes, creates images for each meal, and reads instructions out loud, all from your own code.
So that by the end, you’ll be ready to prototype your own apps and ship them fast using the same tools used by top startups.
Build real-world apps using Google’s Gemini API
Gemini is Google’s answer to GPT, and as an AI developer, learning how to work with it gives you a major edge. That's why this course shows you how to use Gemini’s Pro and Vision models to build apps that understand both text and images, giving you hands-on experience with some of the most advanced multimodal AI models available today.
Average time to complete: 3 days
You'll learn how to generate responses using Gemini Pro, interact with images using Pro Vision, and fine-tune your outputs using prompt engineering techniques. More importantly, you’ll apply those skills in three practical projects that go way beyond theory: a conversational chatbot, a “talk to an image” app, and an intelligent image organizer that sorts files based on what’s in the picture.
If you’re aiming to build modern, multi-modal AI tools or just want to future-proof your skill set as LLM ecosystems diversify, this is a must-have.
Build and scale generative AI apps with AWS Bedrock
Most developers start out by integrating models from OpenAI or Anthropic into their apps. But what if you could access models from all the major AI companies like Meta, Mistral, Cohere, and Stability AI all from a single API?
That’s exactly what AWS Bedrock lets you do:
Average time to complete: 7 days
This course teaches you how to build and scale production-ready generative AI apps using AWS Bedrock. You’ll build three full projects that use LLMs and Diffusion Models to generate text, summarize meetings, write code, and even create images, all using AWS’s secure, serverless infrastructure.
You’ll also get hands-on with tools like API Gateway, Lambda, and S3 which are core to deploying scalable AI systems in the real world. And because everything runs serverlessly, you’ll learn how to ship fast without worrying about managing infrastructure.
By the end, you’ll have built real generative AI apps that can be deployed, scaled, and customized with full control over which models you use and how you use them. It’s one of the best ways to go from “just calling an API” to actually building AI apps at scale.
Build scalable AI apps with LangChain and Pinecone
If you're ready to go beyond simple prompts and start building real AI applications that respond intelligently and stay relevant, this course is the natural next step:
Average time to complete: 2 days
You’ll learn how to use LangChain to connect large language models like GPT to live data sources, persistent memory, and custom logic, giving your apps far more context and flexibility.
You'll also use Pinecone, a high-performance vector database built for AI use cases like semantic search and retrieval-based question answering. These tools together allow you to create applications that can reference external documents, personalize answers, and adapt to new information on the fly.
If you’re serious about building useful, production-ready AI tools, this course gives you the foundation to do exactly that.
Build frontends for your AI apps with Streamlit
If you've built an LLM-powered application or are learning how to, the next step is making it accessible. That means creating a user interface people can actually interact with:
Average time to complete: 3 days
This course shows you how to build an app using Streamlit, which is a fantastic Python framework that turns your models into interactive, shareable web apps in minutes.
Build a production-ready AI chatbot with Nuxt, TypeScript, and the OpenAI Assistants API
If you've already explored building AI apps with LangChain, Streamlit, or the OpenAI API, this course is your next step toward shipping polished, maintainable products using modern web frameworks.
Average time to complete: 3 days
It teaches you how to build a fully functional support chatbot using Nuxt and TypeScript. More importantly, not just the backend logic, but the full experience with a responsive UI, structured codebase, and memory-powered conversations.
You’ll learn how to work with OpenAI’s Assistants API to manage multi-turn threads, handle file uploads, pass context, and define assistant behavior, all while wrapping it in a fast, production-ready frontend. This is ideal if you're aiming to build client-facing AI tools or want to bring Vue-based frontend skills into your AI development workflow.
Expand your web dev skills
And finally, if you want to round out your developer skills and work across the entire stack, this course gives you a complete foundation in JavaScript, Node.js, databases, APIs, deployment, and more.
Average time to complete: 90 days
What are you waiting for? Become an AI Developer today!
There’s never been a better time to train and become an AI Developer, so go ahead and check out our AI Developer career path now, follow along with it, and get hired.
Become a AI Developer
20 milestones 17 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 Developer from scratch and actually get hired.
Earn on average per year:
$155,257
US salary data collected from Indeed, LinkedIn, and Web3.career 2026.
If you can only manage 1-10 hours a week, you can still start this career within 12 months. Even quicker if you can spend a few more hours.
What do you have to lose? Get started now!
P.S
Want some good news?
Every course that I just shared is all included in a single ZTM membership. Once you join, you get access to all of them and more.
Better still?
You also have access to the private ZTM Discord community, where you can ask questions from your teachers, as well as other students and current working AI Developers.
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