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Medhovarsh Bayyapureddi
Intern Machine Learning & Full-Stack Engineer specializing in computer vision and healthcare AI
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AI/ML-focused backend engineer who shipped two production systems: PersonaPal (agentic LLM chatbot with RAG, FAISS-based retrieval, and Redis semantic caching) and CervixScan (clinical diagnostics platform with PostgreSQL data modeling and human-in-the-loop safety for low-confidence predictions). Demonstrates strong performance/reliability work (indexed vector search, caching, query optimization to ~200ms) and end-to-end ownership from orchestration design through deployment.
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Key Strengths
- Built production backend for agentic AI chatbot (Python/FastAPI) with RAG and session endpoints
- Performance optimization of vector retrieval (migrated to FAISS indexed search) to reduce latency at scale
- Implemented Redis semantic cache using embedding similarity threshold to cut repeated-query latency and API cost
- Designed persona orchestration and reinforcement layer to prevent context drift and maintain consistent tone
- Designed plan-then-execute agent workflow with step verification and retry/escalation/stop behavior
- Production engineering practices: Pydantic typing, async I/O, Celery+Redis for long-running inference, Poetry/Conda env reproducibility, Pytest, structured logging with p99 latency tracking
- PostgreSQL clinical data model (Patients/Scans/Findings) with constraints; improved slow report queries via composite indexes and EXPLAIN ANALYZE (reduced to ~200ms)
- Operational guardrails for AI in healthcare: monitoring confidence scores, evaluation pipelines, and human-in-the-loop fallback for low-confidence predictions
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