A breakdown of my technical stack and the engineering toolkits I use to build robust, scalable products. I focus on developing clean, modular code and choosing the right framework for the engineering problem at hand.
AI / Machine Learning
Where I direct my research and applied engineering — cleaning datasets, training predictive estimators, and building pipelines using standard frameworks like PyTorch and Scikit-learn to solve real-world problems.
Tools
The systems, runtimes, container engines, and clouds I utilize to package, orchestrate, test, and ship production-ready applications reliably.
Programming
The core programming languages I write in most frequently, selected strategically based on the technical domain. I focus on key software concepts like memory safety, strict typing, speed, and algorithmic efficiency.
Web Development
Building responsive, modern user interfaces and scalable server-side systems. I emphasize semantic layouts, state management, API integration, and fast client-server rendering pipelines.