* Led the design and implementation of a project to build third-party channel (e.g., HBO, AMC) recommendation carousels on Amazon Prime Video. Assembled and coordinated a cross-functional team (front-end, back-end, ML data, device etc.) to successfully launch the channel-specific carousels in addition to the existing 'customers also watched' recommendation. This significantly improved the customer experience and engagement on Prime Video, leading to increased streaming hours and monthly active users.* Independently led the migration of a large-scale recommendation system from an x86 platform to an ARM-based architecture. Leveraged Amazon Profiler and other resources to thoroughly research and fine-tune the service, resulting in a 17% improvement in performance (lower latency, better CPU usage) and a 26% reduction in infrastructure cost.* Designed and led the migration of the legacy large data pipeline to an Amazon SageMaker ML pipeline to generate the core title relevance data powering multiple recommendation services for Prime Video. Collaborated closely with the data science team to build the infrastructure for large-scale streaming data processing and model training. Routinely maintained the ML pipeline, implementing new features to optimize its availability and performance.* Designed and implemented multiple features for the largest distributed recommendation carousel service (over 500K TPS across regions) on the Prime Video home and detail pages. Took responsibility for the operational work to closely monitor the alarms and performance, proactively implementing optimizations to ensure the service’s health.

* Working closely with clients (Toyota, Ford, Nissan), project managers, and UX designers to design and build business intelligence web applications with automated reporting and data visualization capabilities. Played a pivotal role in architecting and implementing the back-end APIs and front-end user interface, enabling clients to gain valuable data-driven insights into their business operations. * Leveraged AWS services to develop a suite of internal tools that significantly improved the efficiency of the marketing team. This included an automated email scheduling system, a marketing campaign management system, and a secure file storage system. * Design, implement and maintain an automated data pipeline for collecting and processing daily customer data.