- Led the design and implementation of an extensible Python-based automated ingestion service to streamline the generation of media artifacts, utilizing AWS technologies such as Lambda, SQS, and SNS.

- Developed and integrated an internal Python library within Prime Video to generate media artifacts, which are leveraged by teams such as Fire TV to enhance the overall Amazon customer experience.- Collaborated with multiple Amazon Ads teams to implement a largely automated Python ingestion process, handling 100k requests weekly and generating databases for downstream services. This resulted in a 3,200% increase in database size and a 98% reduction in process runtime, leading to a 550% improvement in overall cloud service performance.- Mentored multiple software development engineer interns, helping them successfully complete their summer projects. These projects primarily focused on driving operational excellence and automation of manual tasks.

- Implemented a backend Python daemon supervisor that processes SQS messages to automate job execution. This solution successfully replaced a manual process that previously consumed several developer hours each week, significantly improving efficiency and reducing operational overhead.- Implemented Java code to partition an in-memory database into multiple segments, reducing the overall database size and improving the latency of an Alexa Cloud Service by approximately 3x.

Developed an iOS Watch application by integrating it with an existing iPhone app using Swift. Focused on delivering a seamless and intuitive user experience while ensuring efficient performance across both devices.

Developed a clustering machine learning algorithm to enhance the productivity of support engineers, resulting in a 20-30% increase in efficiency.