At AIMLSE, I resolved three critical bugs in a bidirectional block-to-Python converter, bringing its passing test suite from 27/30 to 30/30. I also traced cascading integration failures across a complex, multi-module Python codebase.
I identified exposed credentials and misconfigured CORS middleware at AIMLSE and remediated those vulnerabilities. My backend work includes designing and building a Library Management System with FastAPI, SQLAlchemy, and JWT authentication.
For my Customer Churn Prediction API project, I built and deployed an ML inference service using a scikit-learn preprocessing pipeline and an XGBoost model. I exposed it through a validated FastAPI endpoint, containerized it with Docker, and deployed it to Render.
I contributed merged changes to Hugging Face Transformers, improving the Wav2Vec2 processor API and its documentation. I also contributed to OCaml-API Watch, extending API-diff logic to detect class-level changes; before this, I supported enterprise customers at SolarWinds by investigating system logs and troubleshooting technical issues.
