I build retrieval and LLM systems that actually ship. Currently at Asurion replacing a 3-year-old semantic search with a modular RAG pipeline serving 10,000+ daily queries, while finishing my M.S. in Data Science & AI at USF.
I'm an AI Engineer based in San Francisco. My day-to-day is RAG plumbing: ingestion, chunking, embeddings, retrieval, evals — making sure the thing that comes out the other end is actually better than what was there before.
Right now I'm at Asurion, where I built a modular RAG system that replaces a three-year-old semantic search serving 10,000+ daily customer queries. Before that I shipped a React Native app at StudyStudio.ai, an NLP matching system at USF's Data Institute that's still in production, and time-series pipelines for residential energy research at Frontier Energy.
Outside of work I ran USF's 100+ member rock climbing club for two years. I like things that are precise, durable, and slightly understated.
End-to-end pipeline that synthesizes connected-vehicle transactions, lands them in Postgres, runs feature engineering and a fraud-detection model, and surfaces results in a real-time Streamlit dashboard.
Implementing a transformer-based LLM in PyTorch from first principles, following Sebastian Raschka's Build a Large Language Model. A working understanding of every line, not just the API.
U-Net CNN for image segmentation that automates cell counting for biomedical research. Mean error of 1.4 cells, beating the 3-cell target by 2x and replacing manual counting.