• Led a team of 4 engineers to deliver Machine Learning projects that generate real-time recommend services and user persona for AWS users. Unified the data source for consumption by all AWS Console teams who saw a 20% jump in new user activities in the Beta rollout.
• Designed a unified data service across all AWS teams, emitting data for personalized experience on different consoles. Captured data from RedShift into S3 using Hammerstone SQL jobs and built a data pipeline with Python Glue to process the data. Ran a SageMaker ML inference model via Java AWS Lambda and used DynamoDB as the model inference storehouse
• Added a data backfill workflow in Java for data sanity and collected metrics on Cloudwatch dashboards to trigger alarms for polluted data
• Opt-in Region Expansion: Working on expanding the Personalization service in multiple regions. Reduces service latency, mitigates data security concerns and provides a one-click mechanism to expand the service to any new AWS region in the future.
• Participated in code reviews and design discussions. Handled on-call duties for personalization and data engineering teams.
Technologies:
Java, DynamoDB, Lambda, Simple Storage Service (S3), Simple Queue Service (SQS), Glue, Simple Notification Service (SNS), SageMaker, Identity & Access Management (IAM), CloudWatch, OpenSearch
• Enhanced performance of AWS OpenSearch, an open source version of ElasticSearch in Java. Allowed deletion of read-only searchable snapshot indices and guarded snapshot backing indices from deletion. Fixed the search API for datetime input parameter.
Technologies:
Java