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Ramya Latha
Senior AI/ML & Data Engineer specializing in Generative AI and RAG systems
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About
GenAI/RAG engineer who has deployed a production policy/regulatory search assistant for a financial client using LangChain + Vertex AI, FastAPI, Docker/Kubernetes, and Airflow-orchestrated data pipelines. Demonstrated measurable impact with 50–60% latency reduction and 70% fewer pipeline failures, plus KPI-driven grounding evaluation (90%+ target) and strong cross-functional collaboration with compliance/business teams.
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Key Strengths
- Built and deployed production RAG GenAI assistant for financial/compliance policy & regulatory document search
- Latency optimization via caching, chunking/retrieval tuning, async batching and parallel retrieval (50–60% end-to-end response time reduction)
- Reliability and anti-hallucination approach using source grounding, RBAC, and observability
- Orchestrated end-to-end RAG/data pipelines with Airflow across RDS/MongoDB ingestion, Spark/Glue validation, embedding generation, and index refresh
- Improved pipeline reliability with retries/SLAs/alerts (70% reduction in pipeline failures)
- KPI-driven evaluation of GenAI systems (e.g., 90%+ grounding accuracy) with monitoring, sampling, and user feedback loops
- Effective collaboration with non-technical compliance and business stakeholders through demos and clear translation of requirements
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