AI Engineering is the discipline of building, deploying, and maintaining production-ready applications powered by foundation models (large language models, vision models, and multimodal systems). Unlike traditional machine learning, AI engineering focuses on integrating pre-trained models through techniques like prompt engineering, retrieval-augmented generation (RAG), and fine-tuning rather than training models from scratch. The field emerged rapidly after 2022 as organizations shifted from research-focused ML to practical AI application deployment. The key challenge is not just making models work, but making them reliable, cost-effective, and observable in real-world production environments where latency, hallucinations, and context limitations matter.