The skills AI engineers use on the job, from programming and math foundations through machine learning, deep learning, LLMs, building AI applications, data engineering, MLOps, evaluation, responsible AI, and scaling. Start at the top and work down, basics first. Free to read, no signup required.
How to use this: tap a step on the map to open its details, skill pills, and guidance in a side panel. Work down the spine in order; each stage assumes the ones above it. The field moves fast, so anchor on the concepts that last and build real projects, because that’s what turns AI tinkering into engineering.
Build and evaluate real AI projects
AI engineering is learned by building, and just as importantly by evaluating what you build. A couple of real projects that you can measure and reason about prove far more than any certificate in a field this new.
A RAG app over your own documents, with a real evaluation set to measure answer quality.
A small agent that uses tools to complete a multi-step task, with guardrails and error handling.
Fine-tune or adapt a smaller model for a focused task, and measure whether it actually beats prompting.
Take one project to production: serve it behind an API, monitor cost and latency, and track quality over time.
Put it on GitHub with a README that reports how you evaluated it. The eval shows you’re an engineer and not a prompt tinkerer. Interviewers want to hear why your system works and how well.
Frequently asked questions
The lines are blurry and vary by company. Broadly: data scientists lean toward analysis and modelling to answer questions; ML engineers focus on training and productionising models; AI engineers mostly focus on building applications on top of foundation models (LLMs): RAG, agents, evaluation, and reliable delivery. This roadmap covers the fundamentals under all three so you can move between them.
No, especially for the fast-growing applied end of AI engineering, which is about building reliable software on top of existing models. A strong grasp of the fundamentals here, plus real projects, matters more than a specific degree. Research roles that train new models from scratch are where advanced degrees are more common.
Enough for intuition, not proofs. Linear algebra (vectors and matrices), probability and statistics, and a conceptual grasp of gradients and optimisation are what help you evaluate and debug models. You can go a long way in applied AI engineering with working intuition rather than deep theoretical mathematics.
Build the foundation first. It’s tempting to jump straight to LLMs, but concepts like evaluation, overfitting, and generalisation come from classic ML, and they’re what make an AI system reliable instead of just a good demo. Learn the fundamentals, then apply them to LLMs.
It depends on your starting point, how much time you put in, and above all on real projects. Someone with a strong software background can move into applied AI engineering faster than someone starting from zero programming. Building and evaluating real AI projects speeds it up.
Some will. The field moves quickly, which is why this roadmap focuses on concepts that last (evaluation, retrieval, reliability, responsible deployment) over any single tool or model. Anchor on the fundamentals and new tools become easy to learn as they arrive.
Get a personalized plan for real interviews
This page covers what to learn. When you’re ready to get hired, Interview Ready turns it into a personalized 30-day plan built around your resume and a specific target role: real practice in the right order (LLM and prompt-engineering fundamentals, RAG and retrieval, AI system design and evals, MLOps), a guided Build-a-Project track alongside it, and progress tracking the whole way. Start free.
