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Shanmukh Sai Madhu
Mid-level Data Engineer specializing in real-time pipelines and cloud analytics
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About
Researcher from the University of South Dakota who built a production medical RAG system to help interpret model predictions by retrieving relevant clinical notes and medical literature, overcoming retrieval accuracy and imaging-dataset challenges through semantic chunking and metadata-driven indexing. Also has hands-on orchestration experience with Airflow and Azure Data Factory, plus a pragmatic approach to LLM evaluation and stakeholder-driven iteration.
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Key Strengths
- Built and deployed a medical RAG workflow supporting multiple clinical studies
- Improved retrieval relevance via rigorous preprocessing, semantic chunking, and metadata-filtered indexing
- Production-grade orchestration experience (Airflow, Azure Data Factory) with alerts, retries, and dependency management
- Reliability-focused pipeline operations: isolate failures, rerun failed steps, and prevent bad data with early validation
- Practical AI evaluation approach using edge cases, logging, iterative prompt/logic refinement, and ongoing metric monitoring
- Effective collaboration with non-technical stakeholders to iteratively refine LLM outputs and automate workflows
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