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Sivapriya Rachakonda
Mid-Level Software Engineer specializing in cloud-native microservices on AWS and Kubernetes
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Backend engineer who built a stateless Python/Flask service supporting a healthcare-document ETL pipeline, offloading heavy processing to Celery workers and adding strong observability (metrics, structured logs, audits). Demonstrates practical performance/reliability work: batch chunking, priority queues, autoscaling by queue depth/CPU, DLQ routing, and PostgreSQL tuning (indexes, pagination) to cut slow API responses. Also has experience deploying real-time ML classification via TensorFlow Serving behind a FastAPI wrapper and integrating models via REST/gRPC.
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Key Strengths
- Designed stateless Flask APIs with heavy processing offloaded to Celery workers for independent scaling
- Improved ETL reliability with validation rules, schema enforcement, and dead-letter queue handling
- Optimized throughput using batch chunking, priority queues, and autoscaling based on queue depth/CPU
- PostgreSQL performance tuning via slow-query analysis and composite/partial indexing
- Reduced API latency by eliminating full table scans through pagination on large tables
- Deployed ML inference services using TensorFlow Serving with a lightweight FastAPI wrapper; experience with REST/gRPC integration
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