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Laxminarayana Yaga
Mid-level AI/ML Engineer specializing in Generative AI, RAG, and MLOps
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About
Built and deployed a production RAG pipeline at PNC Financial Services to let risk/compliance analysts query millions of internal financial documents in natural language, reducing manual search and speeding regulatory validation. Demonstrates deep practical experience with large-scale document ingestion/OCR cleanup, retrieval performance tuning (hierarchical indexing, caching), and LLM reliability controls (grounding, citations, abstention), plus cloud orchestration on Azure and AWS.
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Key Strengths
- Built and deployed production RAG system for financial analysts (5M+ records ingested)
- Expertise normalizing noisy multi-format enterprise documents (PDFs, emails, loan files) with OCR correction
- Performance optimization for large-scale retrieval (hierarchical indexes, caching, batching, context tuning)
- Reliability/safety controls for LLM apps (grounding, citations, no-answer rules)
- End-to-end ML/LLM orchestration and deployment across Azure and AWS (SageMaker/Lambda/CloudFormation) with CI/CD
- Structured agent/workflow evaluation approach (unit tests, automated metrics, human review, adversarial testing, production monitoring)
- Strong stakeholder collaboration translating non-technical compliance/risk needs into usable AI tools via rapid prototyping
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