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Krishna Kandlakunta
Mid-level Data Scientist specializing in MLOps, LLM/RAG applications, and deep learning
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About
Built and deployed a production compliance automation RAG system (at Citi) that generates citation-backed, schema-validated risk summaries for regulatory document review. Emphasizes regulated-environment reliability with retrieval-only grounding, abstention, confidence thresholds, and immutable audit logging, plus orchestration using LangChain/LangGraph and Airflow. Reported ~60% reduction in compliance review effort while maintaining high precision and traceability.
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Key Strengths
- Built and deployed a compliance-focused RAG platform automating regulatory document reviews
- Strong hallucination mitigation via retrieval-only grounding, abstention behavior, and mandatory citations
- Auditability in regulated environments through full request logging and immutable audit trails (prompt/model versioning, retrieval metadata)
- Production optimization for latency and cost using model routing and caching
- Designs reliable agent/workflow systems with structured outputs, scoped tool access, retries, and guardrails
- Rigorous evaluation approach using gold datasets, regression/edge-case testing, A/B tests, and canary releases
- Defined and tracked grounded answer rate; improved quality by tightening retrieval thresholds and enforcing citation-backed outputs
- Delivered measurable impact: ~60% reduction in compliance review effort/time with improved consistency and traceability
- Effective collaboration with non-technical compliance/risk stakeholders by translating requirements into measurable metrics and HITL controls
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