Building reliable AI systems at the intersection of machine learning, natural language processing, and research.
I develop applied AI systems with an emphasis on grounded data, rigorous evaluation, and practical use. My work connects machine learning engineering, NLP, LLM-based systems, data workflows, and research methodology.
I transform data and ideas into complete AI workflows, from preparation and modeling to integration and deployment.
I assess systems through reproducible experiments, meaningful metrics, careful validation, and critical analysis.
I translate complex technical work into clear documentation, research outputs, and accessible explanations.
- Machine Learning and Deep Learning
- Natural Language Processing
- Large Language Models and Generative AI
- Retrieval-Augmented Generation
- AI Agents and Multi-Agent Systems
- Data and Research Pipelines
- Model Evaluation and Optimization
- Languages: Python, SQL
- AI and Data: PyTorch, TensorFlow, Scikit-learn, Hugging Face Transformers, XGBoost, pandas, NumPy
- LLM Systems: LangChain, ChromaDB, CrewAI, Model Context Protocol
- Engineering: FastAPI, REST APIs, PostgreSQL, SQLite, Docker, Git, GitHub