1) Developing core Tier-1 low-latency, High Traffic (50,000 QPS+), distributed orchestrator services for Amazon Rufus, a Gen-AI assistant responsible for: -Handling evidence retrieval, query planning, and inference pipelines for high-volume, real-time queries. -Managing caching and data warehousing to optimize performance across large-scale datasets. -Supporting 230M+ US customers and 80M+ international users with sub-millisecond response times across globally distributed systems.2) Building the next-generation API platform using Project Reactor with a fully reactive streams architecture: -Migrating synchronous APIs to a non-blocking, event-driven model for improved scalability. -Implementing back-pressure handling and event-driven workflows to enhance system resiliency under load.3) Designing platform features for seamless client onboarding and performance monitoring: -Delivering real-time metrics, dashboards, and alarms for Transactions Per Second (TPS), Availability, and Latency—all out-of-the-box. -Enabling fine-grained observability through custom SLAs, anomaly detection, and proactive alerting to ensure high availability and performance.

Amazon Search

Software engineer in the Insights team with Amazon Halo. The Insights engineering team owns the Insights on Halo Home page, which surfaces content (Feed cards, Weekly Summary Reports) that connects customers’ Halo-tracked behaviors to their impact on overall wellness. Insights provide precise, believable, and relevant suggestions for customers to modify their behavior and change those personal outcomes for the better.

FACTS Automation Service:•Worked closely with the Bio-Statistics team to build a production-level REST service to generate, modify, and parse proprietary format XML. The XML files are used as config files for running adaptive clinical trial simulations. Achieved around 66% reduction in config-file generation time.

Developed the core back-end infrastructure that powered a mission-critical application used by top attorneys across the United States and Canada. I contributed to the development of a highly available and scalable microservices architecture, implementing robust message queuing systems and event-driven architectures. I worked extensively with containerization and orchestration tools like Docker and Kubernetes for streamlined deployment and management. Additionally, I led the successful migration and deployment of the back-end services to the Amazon Web Services (AWS) cloud platform, leveraging services like EC2, ELB, Auto Scaling, and RDS to ensure high availability, scalability, and fault tolerance.