Koti Sai venkata Bhargav Edupuganti

About

Built and deployed a GPT-based RAG enterprise search system for healthcare clinicians, emphasizing low-latency performance and reduced hallucinations while maintaining end-to-end HIPAA compliance. Demonstrates deep applied experience with PHI-safe data governance (detection/redaction/de-identification), secure Azure ML deployment patterns, and orchestration of production LLM workflows using LangChain and Airflow.

Experience

Education

Key Strengths

• Built and deployed HIPAA-compliant GPT-based RAG enterprise search for healthcare teams
• Strong PHI/PII safety approach: automated detection (regex + ML NER), redaction, de-identified-only embeddings/prompts
• Production security controls: RBAC, encryption in transit/at rest, VPC-isolated Azure ML endpoints, audit logging
• Designs modular agent/workflow architectures with independent testability (retrieval, reasoning, validation, output)
• Rigorous evaluation methodology using benchmark datasets, synthetic edge cases, and prompt-level unit tests
• Operational focus on measurable metrics (accuracy, latency, hallucination rate, retrieval precision) and ongoing monitoring (drift, failed queries, response quality)
• Effective collaboration with non-technical clinical operations stakeholders via prototypes and translating workflows into AI requirements