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Bandla Sai Giridhar
Mid-level Software Engineer specializing in full-stack and cloud-native microservices
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About
Backend engineer who built a Python/Flask system for high-volume healthcare claims processing, using PostgreSQL as the source of truth and RabbitMQ workers for scalable async processing. Experienced in SQLAlchemy/Postgres performance tuning, multi-tenant data isolation (including Postgres RLS), and integrating/versioning ML model services (scikit-learn/PyTorch/Hugging Face) with controlled rollouts. Drove measurable performance gains by batching background jobs and adding Redis caching (40% less workload; response times cut from ~10s to 2–3s).
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Key Strengths
- Designed high-throughput Flask API with heavy processing offloaded to RabbitMQ workers
- Data consistency pattern using DB as source of truth with status transitions and id-based messaging
- SQLAlchemy/PostgreSQL performance tuning (indexes, composite keys, joinedload to avoid N+1)
- Kept APIs fast at scale via pagination, selective column loading, and precomputed summaries
- Integrated ML model services (scikit-learn/PyTorch/Hugging Face) behind REST APIs with prediction logging for monitoring and A/B tests
- Model/version rollout strategy using Git + artifact versioning, v1/v2 endpoints, health checks, and controlled traffic switching
- Multi-tenant data isolation using tenant_id filters, PostgreSQL RLS, and schema/DB-per-tenant when needed
- Improved background processing throughput by batching/scheduling jobs and adding Redis caching (40% workload reduction; latency 10s to 2–3s)
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