LLM Engineer & Data Scientist
Classical ML, deep learning, and LLMs—RAG, fine-tuning, and production systems from data pipelines to deployment
I'm an LLM engineer and data scientist based in New York, with experience in large language models, retrieval-augmented generation (RAG), fine-tuning, and production ML systems. I build and deploy AI-powered applications using Python, FastAPI, Docker, and cloud platforms. My ML background spans classical methods and modern deep learning—supervised and unsupervised models, time series and forecasting, NLP, and recommendation-style problems—alongside enterprise-scale data systems.
I've shipped models end-to-end in domains like fraud and transaction analytics (classification, anomaly scoring, behavioral signals), automated data pipelines with Python, SQL, and Spark, and I currently support graduate courses in LLMs, NLP, and applied ML—helping others ship RAG pipelines, fine-tune transformers, and deploy AI responsibly.
I hold an M.S. in Data Analytics & Visualization from Yeshiva University and a B.Sc. (Honors) in Business Studies & Computing Science from the University of Zimbabwe. I'm open to roles where data, ML, and LLMs drive real impact.
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Career journey
From full-stack and applied ML—tabular, time series, and production modeling—into teaching and LLM engineering. Each role layered new tools on a broad ML foundation.
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