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AI engineer roadmap: what to learn and in what order
An AI engineer roadmap across eight skill domains: what to learn first, what each role needs, and what you can skip for the job you want.
6 min read
Most people study too much of the wrong thing. These guides set out what an AI engineering role needs, in the order that makes each topic easier than the last, with plain explanations of the ideas interviews return to.
10 guides
An AI engineer roadmap across eight skill domains: what to learn first, what each role needs, and what you can skip for the job you want.
AI agents explained in plain terms: how tool use and the agent loop work, which guardrails matter, and when a fixed workflow is the better choice.
AI portfolio projects that earn interviews share four things: real data, honest evaluation, deployment and a clear README. Project ideas for each AI role.
LLM evaluation in practice: build a golden set, choose metrics, use LLM-as-judge with care, run regression tests and connect offline scores to online results.
Machine learning fundamentals that interviews return to: generalisation, bias and variance, data splits, leakage, baselines and choosing the right metric.
Maths for machine learning by role: how much linear algebra, probability, statistics and optimisation each AI job needs, and what you can safely skip.
MLOps basics for engineers: versioning code, data and models, CI/CD for ML, serving patterns, monitoring and drift, and what changes for LLM applications.
Python for AI engineers is a small subset: type hints, packaging, async, testing, NumPy and Pandas. What to learn, what to skip and what interviews check.
SQL for machine learning interviews: the joins, window functions and aggregations that come up, and how to write feature queries that do not leak the future.
What is RAG? A plain explanation of retrieval-augmented generation: the pipeline, chunking, retrieval, re-ranking, evaluation and the ways it fails.