Cracking Your First AI Engineer Interview

About this book

Second Edition, September 2026. (The first edition was published as Becoming an AI Engineer.)

AI engineering interviews are changing. Knowing the terminology is no longer enough. You are expected to explain why you chose an architecture, how you know it works, where it can fail, what evidence supports the design and what you would change when the evidence changes.

This is not a collection of interview questions or prompt recipes. It is a practical guide to the engineering judgement behind modern AI systems: when AI is appropriate at all, when a prompt is enough, when retrieval is justified, when tools or agents add real value, and when a simpler deterministic design is the better answer.

What’s inside

• 32 chapters on context engineering, RAG, tool calling, agentic workflows, evaluation, grounding, hallucination, reliability, human review, security, privacy, observability, latency, cost and AI product experience
• Labs that produce evidence: define boundaries, test failure conditions, evaluate behaviour and justify trade-offs
• Six portfolio-grade AI projects covering decision support, evidence grounding, multimodal analysis, operational intelligence, agentic workflows and production controls, each one a system you can defend in an interview
• Engineering with Claude Code: a disciplined loop from specification to tested code that transfers to other AI coding assistants
• Interview preparation: packaging portfolio evidence, defending architecture choices, walking through failure paths, system design, take-home and live-build exercises

Who it’s for

Students, developers, testers, analysts, data professionals and product-minded builders preparing for their first AI engineering role. No machine-learning research background required.