AI Solutions Architect

Many enterprise generative AI initiatives are moving beyond pilot deployments. The challenge is increasingly not whether a system performs in a prototype, but whether it meets the requirements for production approval. Architects must provide evidence that a probabilistic system is grounded, permission-aware, auditable, cost-effective, and operates within defined safety constraints for review by security, legal, platform, and finance teams. I built this course around a pattern I saw repeatedly while co-founding and leading engineering at Educative and during my years at Microsoft: teams understood models and prompting but struggled to produce the artifacts required for enterprise approval. Working on infrastructure provisioning at Azure taught me that getting a system to work once is rarely the hardest part. The harder task is demonstrating that it can operate under security review, cost constraints, and clear operational ownership. Generative AI systems require the same level of operational and governance rigor. You will work through the decision ladder, data and context architecture, permission-aware retrieval, model routing, agent containment, tool integration, evaluation and release gates, observability, threat modeling, unit economics, and governance. A regulated financial-services scenario provides the continuous case study, and the artifacts accumulate into a portfolio that you defend in the final design review. The goal is to prepare you to lead enterprise AI design reviews using explicit decisions, controls, and evidence.