Foundation Model Engineering: From Theory to Production

This article presents Foundation Model Engineering as a practical, systems-level guide to how modern AI models are built, trained, optimized, and operated in production. For CIOs and technology leaders, the key takeaway is that competitive AI adoption now depends less on API experimentation and more on making informed architectural choices across data, infrastructure, inference, retrieval, alignment, and evaluation—choices that directly affect cost, latency, reliability, and business value. It also signals that IT organizations will need stronger cross-functional capabilities in model operations, governance, and performance engineering to safely scale foundation model use across products and workflows.