A complete, honest roadmap for the skills AI engineers actually use, from programming and math foundations through machine learning, deep learning, LLMs, building AI applications, data engineering, MLOps, evaluation, responsible AI, and scaling. It runs top to bottom, foundational to advanced, so you always know what comes next. 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 durable concepts 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 is what shows you’re an engineer, not merely a prompt tinkerer. Being able to say why your system works, and how well, is the whole game.
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 increasingly 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 are exactly what separate reliable AI systems from impressive demos. Learn the fundamentals, then apply them to LLMs.
It depends heavily on your starting point and how much time you invest, and far more on real projects than any fixed number. 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 is what accelerates it.
Some will. The field moves quickly, which is exactly why this roadmap emphasises durable concepts (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.
Ready to prepare for real interviews with a personalized plan?
This roadmap is the map. When you’re ready to actually 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.
