James Staud
Devops Architect - Gen Ai Platforms @Mouser Electronics
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WORK HISTORY
Devops Architect - Gen Ai Platforms @Mouser Electronics
Over the past couple years at Mouser Electronics, I’ve been focused on a problem many organizations are still trying to solve:How do you *operationalize AI* at the enterprise level?Not as a one-off chatbot or isolated pilot—but as a scalable, governed, production-ready platform.Like many large orgs, we had strong teams and ideas, but AI efforts risked becoming fragmented—different teams solving similar problems in parallel.So the focus became clear:Build a foundation that enables *everyone* to move faster with AI, safely.That led to the evolution of our internal AI platform—unifying how we approach GenAI, data, and automation.We defined core patterns around:* Model access* K8s + GitOps deployment* API / gateway control layers* Security and governanceFrom there, things started to compound.We were able to:* Launch customer-facing AI systems that improved search and support* Build internal automation that reduced manual effort significantly* Enable teams to experiment without rebuilding infrastructure* Establish governance so leadership could scale AI confidentlyBut the biggest shift wasn’t technical—it was cultural.Instead of:“Can we build this AI feature?”It became:“How do we build this on the platform?”That’s the difference between experimentation and transformation.The biggest takeaway for me:The challenge with enterprise AI isn’t the models—it’s everything around them:* Deployment* Security* Governance* Developer experience* Organizational alignmentThat’s where the real work—and impact—lives.Still early, but excited about where this is going.
EDUCATION
The University of Texas at Arlington
Bachelor's Degree, Computer Engineering
SKILLS
ABOUT JAMES STAUD
I build systems that turn AI into something that actually works in the real world.My background started in robotics, designing perception driven systems that had to operate reliably in messy, physical environments. That meant solving the full stack: vision, decision making, control, and everything around it.Over time, that focus evolved.Today, I design and scale AI platforms that help organizations move beyond experiments and into production. Not just models, but the systems around them: deployment, orchestration, governance, and developer experience.Across robotics, startups, and enterprise environments, I have worked on:* Vision based automation systems operating in real world conditions* AI driven platforms for search, decisioning, and operational insight* Agentic and RAG systems built for production use, not demos* Scalable infrastructure that enables teams to build and ship AI safelyWhat I have learned is this: The hard part of AI is not the model. It\'s everything around it. How it gets deployed. How it\'s governed. How it scales across teams and use cases.(Turns out just call the API is not a strategy.)That is the problem I focus on.I operate at the intersection of AI, systems architecture, and real world execution, bridging the gap between what is technically possible and what actually works at scale.
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