Rupesh Maddukuri

About

Backend-focused Python/Flask engineer with strong performance and scalability experience across PostgreSQL/SQLAlchemy optimization, caching, and async processing. Has implemented multi-tenant data isolation (schema/db per tenant with RBAC and encryption) and integrated TensorFlow-based ML inference behind a Flask REST API using Redis caching, batching, and async execution; reports measurable wins like cutting endpoints from 6–8s to ~2s and increasing throughput 3–4x via Celery queues.

Key Strengths

• Designed maintainable Flask backend with clear route/service/DB separation
• Improved high-concurrency performance via indexing, query optimization, and caching
• Implemented cache invalidation (TTL) and selective cache busting to reduce stale data
• Reduced slow report endpoint latency from 6–8s to ~2s using profiling and ORM/query tuning
• Integrated ML model serving via REST API with in-memory model loading and Redis caching
• Stabilized ML inference under peak load using batching and async processing
• Built multi-tenant data isolation using schema/database per tenant with middleware routing and RBAC
• Scaled background processing by moving synchronous work to Celery+Redis; achieved ms-level API responses and 3–4x throughput
• Implemented reliable async job handling with retries, dead-letter queues, and Prometheus-based monitoring/alerts

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