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Nagarjuna Vaddineni
Mid-level Full-Stack Software Engineer specializing in cloud-native microservices and data pipelines
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About
Amazon backend engineer who built and operated high-scale Java Spring Boot microservices on AWS (EKS/EC2) handling millions of daily transactions, with deep experience debugging p95 latency and database/ORM bottlenecks. Shipped an AI-driven real-time personalization feature by integrating SageMaker model inference end-to-end with low-latency caching and graceful fallbacks, and designed robust order/payment orchestration with retries, compensations, and DLQ-based escalation.
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Key Strengths
- Built and operated Java Spring Boot microservices on AWS (EKS/EC2) serving millions of transactions daily
- Diagnosed and resolved production p95 latency spikes via CloudWatch/Splunk analysis and Hibernate/DB optimizations
- Strong database and ORM performance tuning (indexes, composite indexes, join optimization, fetch strategy fixes)
- End-to-end application-layer integration of ML personalization (SageMaker inference, caching, fallbacks, monitoring) at scale
- Designed reliable multi-step order/payment workflows with idempotency, retries/backoff, compensating actions, and DLQ escalation
- Production Python engineering practices (layered architecture, typing, dependency pinning, pytest, structured logging for on-call)
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