Problem
AI capability is easy to demo and hard to operationalize. Useful systems need context, tool access, memory, guardrails, workflow design, and interfaces that make the model's output actionable.
AI systems case study
LLM workflows and AI tooling for real software work.
My AI work focuses on applied systems: using modern models, local tooling, and protocol-driven integrations to make engineering workflows more capable.
What I work with
- ChatGPT, Claude, Cursor IDE, and AI-assisted development workflows.
- Model Context Protocol integrations and tool-oriented agent surfaces.
- Custom memory framework concepts and reasoning workflow experiments.
- Custom llama.cpp builds and local model workflow exploration.
- Fine-tuning logic exploration and AI image, video, and audio generation platforms.
Hiring signal
This shows I can learn fast, evaluate new tools critically, wire AI systems into real product surfaces, and communicate the difference between a prompt trick and a maintainable workflow.