Vetted Retrieval-Augmented Generation (RAG) Professionals

Anjali Chepuri

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“Built and deployed an LLM-powered financial document processing and summarization platform at Morgan Stanley using a production RAG pipeline (PDF ingestion, embedding-based retrieval, schema-constrained JSON outputs) delivered via FastAPI microservices on Kubernetes. Drove measurable impact (40% reduction in manual review time) and improved factual accuracy for numeric fields by 30% through metadata-aware retrieval, strict schemas, and post-generation validation, with a human feedback loop from financial analysts.”

“Built multiple AI projects end-to-end as a solo developer, including a privacy-focused LLM app that redacts PII before sending prompts to an external model and a LangGraph-based multi-agent triage system for log analysis. Stands out for combining LLM/agent design, deployment troubleshooting, and practical workflow automation with a strong emphasis on privacy and explainability.”