Implement an asynchronous, parallel web service to optimize memory footprint

Deploy machine learning models into production using TensorFlow

Build a real-time monitoring tool to monitor ML pipeline jobs and system health

Implement a microservice to launch a cast receiver on Google Home devices

Build custom cast receiver to adaptively stream audio from backend to Google Home devices

Drove team migration from JavaScript codebase to TypeScript

Employed modern deep learning techniques to infer diseases from clinical notes

Iterated to hyper-optimize the model to be 20% more performant than the baseline

Built web admin console to diagnose system state reducing error handling time for the team

Used caching and lazy initialization to optimize client-side state management