• Technical Lead of AI predictive maintenance application that monitors mission critical assets of multiple Fortune 100 companies (Shell, ExxonMobil) and U.S. Airforce

• 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

• Developed core user workflows for flagship applications using React, JS, Python, CSS

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

• Designed and developed intuitive UX + middleware for core platform features using React, JS, CSS

• 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

• Implemented brain imaging data processing script to prepare MRIs for predictions.

Integrated into data pipeline for prediction models.

• Worked on developing deep convolutional network to conduct brain segmentation

• Developed web-based deployment solutions for machine learning

• Benchmarked machine learning and neural network browser based performance