Summary
Principal engineer with 14 years across infrastructure, DevOps, and platform architecture, now leading applied AI at enterprise scale: local LLM inference (vLLM), agentic and MCP-based systems, governed RAG, and AI cost optimization. Sets direction for model selection, evaluation, and responsible AI consumption while staying hands‑on; track record of architecting provider‑agnostic platforms and multiplying the teams around them.
Highlights
- Designed MCP‑based developer tooling that cut code analysis costs ~70% by feeding models precise, governed context instead of bulk source.
- Architected a schema‑as‑guardrails platform that lets citizen developers generate code safely to company specification, enforcing secure tool and data access through approved containers and reusable patterns instead of one‑off solutions.
Experience
Principal Engineer, Enterprise Systems Engineering
Nike, Inc. – Beaverton, OR | Nov 2021 – Present
- Set enterprise direction for applied AI practices: model selection, retrieval strategy, evaluation and experimentation, MLOps, and responsible AI and model governance for production use.
- Architected a schema‑as‑guardrails platform that lets citizen developers generate code to company specification safely, enforcing secure tool and data access through approved containers, platforms, and reusable patterns rather than one‑off solutions.
- Designed MCP‑based developer tooling (a symbol‑index server) that reduced code analysis costs by ~70% by feeding models precise, governed context instead of bulk source.
- Maintained and operated internally‑hosted LLM inference infrastructure (vLLM) at enterprise scale, serving internal chat and code‑generation workloads.
- Built the framework for enterprise Microsoft Copilot adoption, including Copilot Cowork and Copilot Studio.
- Multiplied teams through architecture reviews, reusable patterns, and mentorship across engineering organizations.
Senior Engineering Manager
Nike, Inc. – Beaverton, OR | Mar 2018 – Nov 2021
- Led the Release Engineering Core team (6 developers, systems engineers, and architects) and its Agile process; owned the global retail release platform.
- Spearheaded quality efforts for high‑demand ("high‑heat") sneaker releases; founded an internal engineer training initiative to grow team capability.
Lead DevOps Engineer
Nike, Inc. – Beaverton, OR | Apr 2017 – Mar 2018
- Architected a containerized, cloud‑provider‑agnostic global release management platform (Azure, AWS, and datacenter deployments) and handed it off cleanly to the owning team.
- Designed a global package distribution system that cut deployment time from 9 days to 1 hour, removing an infrastructure bottleneck and raising release velocity.
DevOps Engineer
Nike, Inc. – Beaverton, OR | Aug 2015 – Apr 2017
- Designed a global retail testing methodology (Ansible + Nutanix) that reduced lab creation from 1 week to 1 hour and release testing from two weeks to under one business day.
- Used infrastructure‑as‑code to guarantee test‑environment integrity; helped other teams automate daily work with Ansible and Python.
Service Virtualization Engineer
Nike, Inc. – Beaverton, OR | Jan 2015 – Aug 2015
- Built company‑wide CA Service Virtualization training and processes, eliminating ~$1M/month in infrastructure spend.
Systems Administrator
Performance Health Technology – Salem, OR | Mar 2014 – Jan 2015
- Maintained the company datacenter (Nutanix Acropolis, Meraki networking, Nagios) and mentored junior administrators and developers.
Helpdesk Manager
Allegheny Technologies Incorporated – Albany, OR | Oct 2012 – Mar 2014
- Supported 2,000+ end users across 5 remote sites; built and deployed systems; mentored technicians and interns.
Armory Chief
United States Marine Corps – Jacksonville, NC | Jan 2009 – Oct 2012
- Accountable for $19M in weaponry and electronics; developed and delivered training to 40,000+ Marines; mentored 15 junior Marines into leadership roles.
Projects
Synaptic Drift: governed context retrieval for LLM agents
open source · in active development
- Retrieval‑augmented generation (RAG) system serving verifiable, governed context to agents over MCP with schema‑enforced tool contracts; ~80% token reduction on documentation/context retrieval in testing to date.
- Validated by end‑to‑end task success: building working functionality against live, unseen library docs using a 9B model, so retrieval quality couldn't be masked by model capability. Sub‑21 ms P95; cryptographic content verification and lifecycle governance.
github.com/trydydd/synaptic-drift
Vamp: schema‑as‑guardrails platform for citizen developers
personal · in active development
- Lets citizen developers write and ship code to company specification safely, enforcing secure tool and data access through schema‑defined guardrails instead of manual review.
Skills
- Applied AI
- Large language models (LLMs), Agentic and multi‑agent systems, Model Context Protocol (MCP), Retrieval‑augmented generation (RAG), LLM inference serving (vLLM), Model evaluation & experimentation, Prompt & guardrail design, Cost optimization & inference efficiency, Responsible AI & model governance, MLOps, Model pretraining
- Architecture & Leadership
- Enterprise AI architecture, Platform architecture, Build‑vs‑buy analysis, Technical direction & standards, Mentorship, Cross‑functional collaboration, Risk management, Agile
- Languages
- Python, Bash, PowerShell
- Platforms & Infra
- Azure, AWS, Docker, Kubernetes, Datacenter, Nutanix, Ansible, Jenkins, Git/BitBucket, CI/CD, Infrastructure‑as‑Code
Education & Honors
- B.A., Thomas Edison State University (2024)
- A.S., University of South Carolina (2006–2008)
- Navy and Marine Corps Achievement Medal