AI engineering is a distinct discipline separate from ML engineering, focused on building dependable production applications on top of LLMs rather than training models. The core challenge is bridging the gap between impressive demos and reliable production systems. Key skills include RAG, evals, agent design, and production reliability. The author introduces the build-eval-improve loop as the central mindset: building an agent is trivial, but continuously evaluating and improving a non-deterministic system is the real job. The hardest part is choosing the right metrics for evaluation. As companies become AI-native, AI engineering is evolving into a team-scale discipline with specialized roles for tool selection, safety, token optimization, and more.
Table of contents
Demos are easy. Dependability is the Job.AI Engineer vs ML EngineerThe Four Skills That Keep Showing UpThe Build, Eval, Improve LoopWhy This Becomes a Whole TeamThe Hardest Part Is Picking the MetricsThe Practice Area, Not the BuzzwordSort: