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Senior Software Engineer with 15+ years experience in the government and enterprise sectors. Expertise in modern frameworks/libraries, modular component architecture, and cloud-enabled services. I specialize in transforming legacy monoliths into maintainable, accessible, and high-performance ecosystems.
I believe that in an era of rapid AI advancement, the most critical skill is Meta-Learning—understanding the mechanics of how we acquire and apply new knowledge.
Currently Building & Learning
My current focus is shifting from "AI-assisted development" to "AI Engineering"—understanding the low level design of RAG, embeddings, and model orchestration.
🤖 PLANTS NLQI — A Natural Language Query Interface for the USDA botanical database. Solving for accuracy in specialized scientific domains using RAG.
📚 OpenSpec Architecture — Experimenting with declarative, YAML-based AI agent definitions to ensure developer-led control over agentic workflows.
💡 Model Translation — Learning to port Python ML models to browser-ready TypeScript/TensorFlow.js to enable high-performance, client-side intelligence.
♿ 508 Accessibility Tools — Prototyping applications that help developers automate and verify Section 508/WCAG compliance in modern SPAs.
A laboratory for problem-solving and meta-learning.
Learning How to Learn: Using challenges as a medium to refine Active Recall and Spaced Repetition techniques in a technical context.
First Principles: Breaking down complex problems into primitive patterns (Graph theory, Dynamic Programming, Sliding Windows) to build a reusable mental library for system design.
Process over Product: Each solution is an exercise in documenting the "why"—translating abstract requirements into clean, performant TypeScript/Python code.