I design the data infrastructure that makes AI systems intelligent — RAG pipelines, vector search, and LLM integrations grounded in reliable, governed data. 5 years in finance-domain data engineering. Databricks Certified GenAI Engineer.
LLMs are only as good as the context they receive. I build the data layer that grounds AI agents in accurate, governed, domain-specific knowledge.
Retrieval-Augmented Generation architectures on Databricks — chunking, embedding, retrieval and re-ranking for finance-domain documents.
Databricks Vector Search & Unity Catalog for governed, low-latency semantic retrieval over large document corpora.
LLM chains and agent orchestration using MLflow, Model Serving, and structured tool-use patterns for data-grounded AI.
Beyond client work, I build things for fun — personal sandboxes for exploring new tech, architectures, and ideas.
You are looking at it! Built with semantic HTML, CSS, and vanilla JS. Documented and deployed via GitHub Pages.
Automated processing and visualization of e-cycling race results. Python scripts to parse and generate graphics.
A fun project exploring cycling events data, built with Streamlit.
E-Sports Championship transparency project.
Ambitious Full Stack AI-platform (SvelteKit + Python). "Failed due to its size" but a massive learning ground for architecture and complex orchestrations.
Automated workflow to sync documentation from multiple projects into a central repository.
Formal certifications and ongoing hands-on achievements.
Design and implement LLM-enabled solutions, RAG applications, and LLM chains using Databricks Vector Search, Model Serving, MLflow, and Unity Catalog.