Data Science & AI
AI / ML
Semantic guardrails for LLMs, generative AI, Retrieval-Augmented Generation (RAG), integrating large-scale knowledge bases with LLMs
Data & BI
Data mining, ETL (Microsoft SSIS), business intelligence, data visualization with R and Tableau
Languages
Web & App Development
Frontend
JavaScript, React (Material UI / MUI), HTML5, CSS3, Bootstrap, Blazor, Progressive Web Apps (Service Workers, Workbox)
Backend
.NET Core, C#, Node.js, Python
Data & Databases
Data / Python stack
asyncio, httpx, BeautifulSoup, pandas — concurrent data collection, cleaning, and scoring
Databases
PostgreSQL, MySQL, MSSQL, Oracle DB, MongoDB, Firebase — SQL and NoSQL across projects
Cloud & DevOps
Cloud
AWS — deploying and running AI and data services
Containers & CI
Docker for reproducible builds and deployments
Practices & Tooling
Engineering hygiene
Git & GitHub, linting/formatting (ruff), testing (pytest), pre-commit hooks
Domains
Open-source contribution, web scraping & automation, GIS / OpenStreetMap, kernel-level HID