Coordinated across 3 teams to understand pipeline needs and security requirements of ML applications

Architected API design to host Hawking Applications dynamically and reduce onboarding time from 2 weeks to 3 days

Developed unified workflow with Python and Java to deploy new applications across 5 feature packages

Redesigned price-based rule generator with C++ and Python to avoid redundant ML model evaluations for 2x speedup

Devised C++ algorithm to cover 60% more relevant target points during rule generation

Expanded metrics and test coverage with C++ for 3 code suites

Coordinated across 2 teams to understand user needs, approve design proposal, and deploy API changes across 5 packages

Coded inter-process graphical linkage with Java and Kotlin to save 700 hours per year of tracking sequential process graphs

Onboarded new service to graph tool with Java and Kotlin to save 80 hours per year of reading raw data logs

Compiled metrics dashboard and employed 2-day canary release to address latency risk of emitting new graph events

Leveraged Guice for utility dependency injections and Mockito for unit testing

Migrated 2 client-facing Angular webpages to TypeScript + React + Redux to improve compatibility with company codebase

Transformed each page’s global initialization workflow into 5+ encapsulated processes

Leveraged Redux-Saga to optimize asynchronous API requests

Coded unit tests in Jest and deployed changes to Azure DevOps pipeline