AI engineering has emerged as a distinct discipline built on three layers: application development, model development, and infrastructure. Unlike traditional ML engineering which focuses on training custom models, AI engineering emphasizes adapting existing foundation models through prompt engineering and fine-tuning. The field requires less deep ML knowledge but more focus on evaluation, inference optimization, and building user interfaces. AI engineers work with larger, more compute-intensive models that produce open-ended outputs, making evaluation significantly more challenging. The role bridges software engineering and ML, with many practitioners coming from full-stack development backgrounds rather than traditional ML research.