Developed the team’s first AI pipeline using Python, internal models and Retrieval Augmented Generation for bug triaging.
Reduced development team hours spent analyzing log dumps by automating irregular behavior semantic searching in 20 GB+ logs using file chunking, recursive summarization, and parallelized model API calls, reducing inference time from ∼36 hours to ∼2 hours.
Implemented an interactive network message visualization feature using C++ and Qt 6 on an internal tool used by 15+ teams that enabled better granularity in log analysis.
Generated charts that abstracted away vendor log format specifics while maintaining a latency ceiling of 100ms per render when generating interactive charts for 20GB+ log files.
Created a Slack bot using Python that synchronizes internal ticket tracking system updates into relevant Slack channels.
