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Apoorva Nanabolu
Senior Data Scientist / Generative AI Engineer specializing in fraud, risk, and MLOps
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About
Built and deployed a production LLM/RAG fraud investigation system to replace manual investigator workflows, combining transaction data, historical cases, and policy documents with agent-style steps and LoRA fine-tuning. Demonstrates strong reliability engineering (grounding, citations, abstention paths), performance optimization (retrieval/indexing/caching), and end-to-end MLOps orchestration using Azure ML Pipelines/MLflow plus Kubernetes/Argo with canary and rollback deployments.
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Key Strengths
- Built and deployed a production LLM-based fraud investigation system replacing manual review
- Designed RAG pipeline over transaction data, historical cases, and policy documents
- Implemented hallucination controls via strict evidence grounding, relevance thresholds, and abstention paths
- Added traceability with structured outputs and citations to retrieved snippets
- Optimized retrieval relevance and response time (embedding/index improvements, caching)
- Strong MLOps/orchestration experience across Azure ML Pipelines, MLflow, Kubernetes, and Argo (canary + rollback)
- Modular agent/workflow design with measurable metrics (grounding, latency, cost, stability) and step-level testing
- Effective cross-functional delivery with non-technical ops/compliance stakeholders using scenario-based evaluation
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