Designed and implemented suite of features that enabled nontechnical users to train, validate, and iterate on AI Models within intuitive UI

Architected Structured + Unstructured Data Querying AI Agent and LLM powered sensor onboarding, speeding up customer onboarding

Achieved a 50% performance gain in core cloud ML training pipeline by optimizing task distribution logic to minimize overhead and maximizing multi-node parallelization.

Led full-stack performance improvements resulting in 90% reduction in UI flows not meeting SLA and 12.5% reduction in expected cost of serving UI

Designed and implemented intuitive and comprehensive time series visualization tools

Implemented internal Bug Triaging AI Agent reducing time to triage bugs by ~20%.

Developed LLM backed bug analysis tool allowing team to understand root cause of 100s of bugs per release

Rearchitectured US Air Force predictive maintenance system on top of the Reliability application, resulting in a more resilient architecture and streamlined development

Implemented integrations with services including Docusign, AWS Lambda, Twilio, and Google Maps.

Redesigned and implemented UI across platform to create a more consistent user experience

Performed database migrations to update UI configurations for all customers

Worked on developing deep convolutional network to conduct brain segmentation

Benchmarked machine learning and neural network browser based performance