Career Path
What to learn to work as an AI engineer, in what order, and why each piece still matters when a coding agent writes most of the code.
The premise
Coding agents changed which skills pay off. Memorizing syntax matters less. Knowing how software actually works matters more, because the agent will happily make bad tradeoffs in latency, reliability or cost unless someone who understands those tradeoffs steers it. That someone is you.
This section is a skills map, not a course. Each page names an area, says what "skilled" looks like, and points at how to practice it with agents in the loop.
Pages
- Software Engineering Fundamentals — Full-stack applications, data, system architecture, security and reliability, production operations. What you need to know to steer an agent that writes your code.
Coming soon
- AI and ML fundamentals — How models work well enough to predict when they fail
- Agentic coding — Working with coding agents as a daily practice
- Building AI applications — Prompts, retrieval, tools, evals, cost
- Working in the field — Portfolios, interviews, what teams hire for
Sources
The framing follows Andrew Ng's AI Engineering Skills Map series, starting with his post on software engineering fundamentals. Pages cite their sources; the analysis and the practice suggestions are ours.