Building a prompt demo is only the beginning. Enterprise AI engineering connects application development, infrastructure, model operations, observability, security and governance.
Foundation and GenAI
Engineers need Python, APIs, cloud fundamentals, containers, Kubernetes, infrastructure as code, ML basics and prompt engineering.
RAG and agents
RAG adds enterprise knowledge, while agent frameworks introduce tool use, workflows, memory and human approval checkpoints.
MLOps and LLMOps
Production systems require experiment tracking, model serving, CI/CD/CT, drift detection, LLM observability, cost controls and responsible AI practices.