The AI Engineering open lab will introduce participants to AI engineering as a distinct and emerging area focused on building applications with foundation models rather than training machine learning models from scratch. Drawing on AI Engineering: Building Applications with Foundation Models by Chip Huyen, the session will explore how AI engineering differs from traditional machine learning engineering by shifting attention from model development to system design, integration, and deployment. Participants will explore what it means to build AI-powered systems using pre-trained models accessed through APIs, and how this shift reshapes engineering roles and responsibilities. Core AI engineering techniques will be introduced at a conceptual level, including prompt engineering, retrieval-augmented generation (RAG), agents, and fine-tuning. The open lab will focus on why these techniques are used, what problems they are designed to solve, and how engineers decide among them in practice. The open lab will also address practical engineering constraints that shape real-world AI deployments, including latency, cost, scalability, and failure modes.