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Geetha Bommareddy
Mid-level AI/ML Engineer specializing in fraud detection and risk analytics in Financial Services
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At JP Morgan Chase, built and deployed a production LLM-powered RAG knowledge assistant to help fraud investigators and risk analysts quickly navigate regulatory updates and internal policies, reducing investigation delays and compliance risk. Strong focus on secure retrieval (RBAC filtering), reliability (layered testing + observability), and production constraints (latency/SLOs), with Airflow-orchestrated, auditable ML pipelines.
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Key Strengths
- Built and productionized an LLM RAG knowledge assistant for fraud/risk investigators in a regulated environment
- End-to-end RAG implementation: ingestion, document quality/versioning, chunking strategy, and retrieval tuning
- Security-first design with RBAC permission filtering before retrieval
- Performance/latency optimization using caching and tuning with production SLOs
- Designed reliable AI workflows with layered testing (unit/integration/offline gold set/online A/B) and strong observability
- Established practical evaluation metrics (task completion, groundedness, citation correctness, precision/recall) tied to user impact (time saved, reduced manual work)
- Built auditable, reproducible ML pipelines orchestrated with Apache Airflow (dependencies, backfills, failure handling)
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