Senior Technical Leader — AI, Supply Chain, Retail & CPG
I build production AI systems at the intersection of enterprise software and emerging technology. 20+ years of experience helping large companies operationalize GenAI, agentic architectures, and data platforms.
- Architecture — Multi-agent systems with human-in-the-loop decision boundaries
- Retail & CPG — Supply chain optimization, product data enrichment, demand sensing
- MCP (Model Context Protocol) — Giving AI agents structured access to enterprise data
- Evaluation — Domain-specific eval datasets and judges for production GenAI
- Technical Leadership — Architecture reviews, decision frameworks, team operating rhythms
| Repo | What it is |
|---|---|
| mcp-enterprise-patterns | Production-grade patterns for enterprise MCP servers — config, structured errors, validation, observability, and a tool registry |
| retail-cpg-ai-architecture-patterns | Reference architectures for AI in Retail/CPG, with a runnable local multi-agent demo (Docker Compose + Floci) |
| supply-chain-mcp-server | MCP server giving AI agents access to inventory, demand, suppliers, and EDI data |
| retail-cpg-eval-datasets | Open, domain-specific evaluation datasets and binary judges for Retail & CPG GenAI tasks |
| genai-enterprise-workshop | Hands-on 90-minute labs for enterprise teams evaluating GenAI |
| agent-bridge-mcp | MCP server enabling cross-environment communication between AI agents via S3 message store |
| technical-leadership-patterns | Operating frameworks, decision models, and team management patterns for technical leaders |
Previously led technology teams across retail, CPG, and supply chain — from startup to enterprise scale. I focus on the gap between AI demos and production systems: the architecture decisions, guardrails, and operating patterns that make AI actually work in regulated, high-stakes environments.
Dallas, TX | LinkedIn