Sivapriya Rachakonda

About

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.

Experience

Education

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