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Prasad Krishna
Mid-level Full-Stack Developer specializing in healthcare analytics and microservices
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Built and maintained an air-quality prediction backend in Python/Flask that serves offline-trained ML models to a React dashboard via JSON REST APIs. Demonstrates strong performance focus across the stack—low-latency inference under load, SQLAlchemy/Postgres query optimization, multi-tenant data isolation, and caching/background task strategies for high-throughput systems.
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Key Strengths
- Designed lightweight Flask backend to serve ML predictions via REST APIs
- Low-latency inference approach (load model at startup, stateless endpoints, multiple workers)
- SQLAlchemy/PostgreSQL performance tuning (indexes, avoid unnecessary joins, pagination, fetch only required fields)
- Multi-tenant data isolation using tenant IDs with query scoping and API-layer authorization
- Improved high-throughput performance via caching and offloading non-critical work to background tasks
- Practical cache invalidation strategy using time-based expiration
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