Machine Learning Engineer · Applied AI / NLP · Software Engineer
M.S. Computer Science (AI & Machine Learning), Mercy University · New York, NY
I build AI-powered products and the software systems behind them—from LLM applications and data pipelines to user-facing web and mobile experiences. My background includes machine learning engineering at Yum! Brands and hands-on work with Python, cloud infrastructure, APIs, and full-stack development.
I'm especially interested in applied AI, NLP/LLMs, ML infrastructure, and forward-deployed engineering: turning promising models into useful, dependable products.
| Project | What I built / explored | Stack |
|---|---|---|
| Jiffy Clips | Short-video app with AI-generated descriptions and tags, authenticated uploads, and a content-discovery foundation. | Next.js, TypeScript, Gemini, Supabase, Clerk |
| YotesApp / OverYonder | Campus event-discovery product spanning web, mobile, authentication, event data, and notification workflows. | React, React Native, FastAPI, Supabase, PostgreSQL |
| Customer Churn Prediction | Supervised learning workflow for preprocessing customer data, training classifiers, and evaluating churn predictions. | Python, pandas, scikit-learn |
| Brain Tumor Classification | Computer-vision exploration using CNNs, transfer learning, and an interactive Streamlit interface. | Python, TensorFlow, Keras, Streamlit |
AI / ML: scikit-learn, TensorFlow, Keras, PyTorch, LLM evaluation, RAG, recommendation systems
Languages: Python, TypeScript, JavaScript, SQL, Java, R
Backend & data: FastAPI, REST APIs, PostgreSQL, Supabase, Snowflake
Frontend: React, Next.js, React Native, Tailwind CSS
Infrastructure: Docker, Kubernetes, AWS, GCP, Git
- Yum! Brands — Machine Learning Engineering: worked on AI assistants, backend/API improvements, and data workflows.
- Mercy University — M.S. Computer Science, AI & Machine Learning specialization (in progress).
- The College of Idaho — B.S. Computer Science; minors in Arts & Design and Sociology.