End-to-End LLMOps Pipeline for Financial AI Services on Vertex AI
Financial institutions experimenting with generative AI hit a wall between prototype and production: a notebook that answers questions about documents is easy, but a governed system with controlled deployments, monitored behavior, and repeatable fine-tuning is an operations discipline of its own. This gap, commonly called LLMOps, is where most enterprise AI initiatives stall, and engineers who can close it are in acute demand.
The intern designs and builds an end-to-end LLMOps platform for that environment. The control plane is a Python FastAPI service through which generative AI applications are deployed, versioned, and monitored on Google Cloud's Vertex AI. The intern integrates a retrieval-augmented generation stack built with LangChain and OpenAI models, backed by vector search over financial documents, and wires CI/CD pipelines so that changes to prompts, retrieval configuration, or models flow through automated testing before reaching production. Monitoring hooks capture latency, cost, and output-quality signals, and the platform supports fine-tuning workflows for adapting models to domain language.
This hard-difficulty project demonstrates the full production discipline of modern AI engineering: cloud deployment on GCP, pipeline automation, RAG at production standards, and the operational thinking that turns generative AI from demo into infrastructure.
