Proposed an architectural improvement to simplify existing development process for building log pulling service; proposed creating a generic, configuration-driven log pulling API, thereby reducing time needed to create new integrations with 3rd party REST APIs

Recruited team and organizational buy-in for the project and led implementation of the API in a multi-phase approach as part of the delivery of two key integrations that were required for large contract renewals

Reduced time needed for developing of a new REST API based log pulling integration by ~90% (2-3 weeks to 2-3 days) through successful delivery of the project

Led cross-team project to architect and implement audit logging of all user-initiated actions in the distributed backend architecture by instrumenting all entry points to GraphQL service and authentication/authorization services

Architected data ingestion of audit logs into the Panther product via Kinesis Data Streams

Implemented a distributed system architecture for ingestion and ETL of industrial IoT site data

Developed the data model and REST API and integrated with a React web application

Implemented a performant microservice architecture with a REST API layer for large scale ingestion, event-driven processing, and data retrieval of industrial site asset geospatial data

Improved ML model inference speed by 40% through asynchronous and parallelization optimizations.

Enabled real-time decision-making by decoupling data ingestion and implementing a session data retrieval REST API.