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Mounika Yalamanchili
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
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About
Built and deployed a production RAG system for financial/compliance teams using GPT-4, Claude, and local models to retrieve and summarize thousands of internal documents with strong security controls (role-based retrieval, PII masking). Drove significant operational gains (30+ hours/week saved, ~35% productivity lift, ~45% faster responses) and orchestrated end-to-end ingestion/embedding/index refresh pipelines with Airflow, S3, and SageMaker while partnering closely with compliance stakeholders on auditability and traceability.
Experience
Education
Key Strengths
- Led design and production deployment of an LLM/RAG compliance document system
- Delivered measurable impact: reduced manual review by 30+ hours/week and improved advisor productivity ~35%
- Reduced query response times ~45% through architecture and retrieval optimization
- Strong hallucination-mitigation approach (hybrid search, re-ranking, semantic chunking, tighter grounding)
- Implemented robust security controls (role-based retrieval filtering, data classification, PII masking) to prevent sensitive-data leakage
- Built reliable, measurable AI workflows with component-level testing, KPIs, and red-teaming
- Experienced orchestrating ETL/ML pipelines in Airflow integrating S3, embeddings/vector index updates, and SageMaker training triggers
- Effective collaboration with non-technical compliance stakeholders; translated auditability/traceability needs into technical design
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