At Schaeffler India, I developed Python automation for recurring reports, reducing processing time from roughly one hour to under one minute. I also independently built a desktop application to process multiple records in under a minute.
I used pandas, NumPy, and Matplotlib for data processing, analysis, and reporting features, and collaborated with a Germany-based stakeholder to deliver a usable engineering tool.
For AutoRAG Studio, I engineered a Python and FastAPI backend that ingests and chunks documents, indexes embeddings for retrieval, and serves source-grounded responses. I wrote 22 automated tests covering ingestion, retrieval, and API behavior.
I also built a coding agent to identify, patch, and re-test bugs in a Python codebase, evaluating it with hidden tests and execution traces. On AgriSense AI, I developed a Flask application for crop recommendations and achieved 95.1% accuracy with Random Forest.
