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Sreedivya Nagalli
Junior AI/ML Engineer specializing in deep learning and full-stack ML applications
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About
Built and operated a production-used RAG-based AI study planner (GPT-4 + FAISS) that handled 250+ concurrent users, with real-world reliability engineering (caching, fallbacks, schema validation, Redis state, monitoring). Also has healthcare data integration experience at Medinet Analytics, standardizing messy EHR/practice-management data with canonical schemas, idempotency hashing, and compliance-grade audit trails.
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Key Strengths
- Shipped RAG-based AI study planner to real users (250+ concurrent peak)
- Designed reliability features for LLM systems (caching, fallbacks, timeouts, validation)
- Built multi-step agent orchestration with discrete pipeline stages and state in Redis
- Strong evaluation/monitoring practice (offline retrieval precision >90%, online stage-level metrics)
- Drove major reliability improvement via ingestion validation (error rate reduced from 8% to <2%)
- Hands-on experience integrating messy healthcare data (EHR/PM systems) with canonical schemas, idempotency, and auditability
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Publications
2 publications
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