AI work splits into roughly a dozen distinct roles across building, researching, running, deciding and governing; this map tells you what each one does day to day, what it needs, and the most realistic route in from where you are now.
A phase-by-phase plan (roughly 6 to 18 months part-time, depending on where you start) from programming foundations to shipping production LLM systems, with what to learn, what to build, and which certifications are worth it at each stage.
AI engineer interviews test whether you can build reliable products on top of models; prepare for five rounds (LLM fundamentals, RAG and retrieval, evals and quality, agents and production, and a practical build) using the sample questions and strong-answer outlines below.
In an AI system design interview you design an LLM-powered system end to end in about 45 minutes; use a seven-step framework (requirements, quality bar, architecture, data and retrieval, model and cost, safety, evaluation and operations) and practise on the three worked examples here.
ML engineer interviews test ML fundamentals, deep learning, ML coding, ML system design and production operations; use the sample questions and strong-answer outlines below to prepare each round, and practise explaining trade-offs out loud.
An index of the AI and machine learning certifications that are still available as of 2026-10 (Anthropic, AWS, Google Cloud, NVIDIA, Databricks, Oracle, GitHub), which ones fit which role, which were retired in 2026, and links to the free study guide for each on this site.