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Surya Vamshi Sriperambudooru
Mid-level AI Engineer specializing in healthcare claims analytics and RAG copilots
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About
Built a production "appeals co-pilot" for a healthcare claims appeals team, combining an XGBoost/logistic ranking model with a Python/LangChain RAG stack (FAISS + Mistral 7B) to surface high-probability appeal wins and speed policy-grounded drafting. Emphasizes reliability and trust: hybrid retrieval with metadata routing, citation/eval scripts, guardrails, and an explainability layer that non-technical stakeholders could understand and override.
Experience
Education
Key Strengths
- Built and deployed end-to-end LLM + ML production system (claims appeals co-pilot)
- Designed claim ranking model using XGBoost + logistic regression for appeal success probability
- Improved RAG quality by reducing retrieval noise via chunking, metadata filters, and hybrid semantic/keyword scoring
- Implemented explainability layer to increase stakeholder trust (transparent ranking reasons)
- Handled sparse-data segments with regularization and fallback rules for model stability
- Established reliability evaluation framework (precision, hallucination rate, latency, regression tests)
- Production-focused LLM engineering (latency, drift, hallucination control, citation sanity checks)
- Effective collaboration with non-technical stakeholders; built overrideable explanation UI/panel
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