Cracking AI Engineering Interviews: A Practical Field Guide to Coding, Systems, ML, and Engineering Judgment by Morgan Vale

Technical interviews for AI engineering roles are changing. The hardest questions are often not exotic algorithms. They are small systems under pressure: an in-memory database with TTL and historical reads, a banking system with pending transfers and account merges, a bounded concurrent crawler, a file deduplicator that must respect I/O limits, or an inference service where batching, queueing, GPU utilization, and latency all collide. Cracking AI Engineering Interviews is a practical field guide to that style of interview. Inside, you will learn how to: - turn vague prompts into interfaces, invariants, and tests; - implement stateful toy applications without losing history; - reason about concurrency, queues, cancellation, and backpressure; - solve trace, tokenizer, cache, deduplication, and data processing problems; - design inference APIs, request batching systems, prompt playgrounds, chat systems, and model deployment flows; - answer ML fundamentals and experiment-design questions with clear engineering judgment; - practice with extensive exercises, hints, solution outlines, and 2-week, 4-week, and 8-week study plans. This book is written for candidates who want more than memorized answers. It teaches reusable patterns behind practical AI engineering interviews: build the simple version, preserve the right state, test the edge cases, explain the tradeoffs, and adapt when the follow-up changes the problem. Read more