Building production-grade AI systems at the intersection of edge inference, LLMs, and security. U.S. Air Force veteran turned engineer, currently a Security & Awareness Analyst at Centene.
I design and ship AI systems that run in production. Edge inference on custom hardware, LLM-powered analytics, distributed architectures over local networks, and the observability layer that makes them trustworthy. I build with the OpenAI and Anthropic APIs, run Llama 3.2 locally, and care about the engineering judgment that separates a demo from a system: latency, false-positive tuning, safety, and architecture that holds up under real load. I work fluently with AI tooling — Cursor, Copilot, local LLMs — as a collaborator, not a crutch.
My foundation is unusual on purpose: U.S. Air Force Security Forces veteran, B.S. in Computer Science, M.S. in MIS with an Information Security specialization (4.0 GPA), Microsoft-certified Power BI Data Analyst (PL-300), Microsoft Power Platform Fundamentals (PL-900), AWS Cloud Practitioner, Google Cybersecurity certified, and 5+ years of hands-on threat analysis. That security-first instinct shapes every system I ship.
Latency budgets, observability, graceful degradation. AI systems that survive contact with real users.
Threat-modeling, prompt injection, adversarial ML, zero-trust thinking baked in from day one.
On-device inference. Zero cloud dependency when the workload doesn't need it. Your data stays with you.
Five years in Air Force Security Forces — mission-critical execution under pressure. It carried over.
Twenty years of building, defending, and now teaching machines to do the same. Every role compounds — military discipline, fleet-scale data systems, enterprise security, and now applied AI.
A live look at what's running on my own hardware right now — production-grade edge AI, distributed inference, and the AI/security research backing it. A polished portfolio site goes here next.
Production-grade surveillance running entirely on local hardware. Reolink PoE camera streams into a Raspberry Pi 5 and Hailo-8 NPU executing YOLOv8s at 25fps with ~100ms inference. GStreamer pipeline, SQLite event logging, Flask REST API (9 endpoints), Telegram alerts, and systemd services. Tuned with persistence buffers and ROI masking to suppress false positives.
Edge system over LAN. Pi #1 handles inference and serves a detection API. Pi #2 runs Ollama (Llama 3.2) and a SOC-themed dashboard consuming that API. Fully on-device with zero cloud dependency and zero exfiltration.
Security Operations Center-style monitoring console with a dark theme, real-time KPI tiles, Plotly visualizations, modal AI assistant, and forensic history view. Streamlit MVP shipped on Pironman Pi; React/JSX migration in flight for production polish via Netlify.
Local Llama 3.2 wired to a context-injection pipeline that feeds live detection stats. Enables natural-language queries about security data entirely on-device.
Active development against the OpenAI and Anthropic APIs. Structured outputs, prompt engineering, agentic workflows, and the production patterns that take a prototype to a system you can trust.
Multi-agent systems built with the OpenAI Agents SDK, CrewAI, LangGraph, and AutoGen. Includes a Deep Research agent and a 4-agent engineering team coordinating real work.
Vector database integration (Chroma, FAISS) for context-aware AI applications. Chunking, embedding strategy, retrieval evaluation, and the observability layer to keep it honest.
React/JSX progressive web app integrating WHOOP biometric data via OAuth2. Real-time sync of Recovery, HRV, RHR, Strain, Sleep, and Calories with a conversational wellness assistant. Deployed to Netlify.
React-based pricing platform for the sneaker collecting community. AI valuation engine and market trend analysis with an editorial dark aesthetic.
This site. Built with Next.js 16, React 19, TypeScript, and Tailwind CSS — engineered through human-AI collaboration in Cursor. Demonstrates the working pattern I apply across every project: clear architectural intent, AI as implementation partner, human judgment on the bar for production quality. Deployed via continuous integration.
Cybersecurity lens applied to AI: prompt injection, adversarial vision attacks against the YOLOv8 pipeline I run in production, and secure LLM deployment patterns. Backed by 5+ years of threat analysis experience.
Detailed case studies — architecture diagrams, evaluation results, and the stories behind the systems — are landing here.
Open to AI engineering roles, contract work, and serious collaborations on RAG, edge inference, and secure LLM systems.