Background
The long way to AI
For more than 20 years, I've built and operated enterprise network infrastructure — routing protocols, wireless architectures, and zero-trust security — while also working across Linux, storage, virtualization, and cloud platforms. As a pre-sales Solutions Architect, I translate technical complexity into business outcomes. Today NeuralConfig builds production AI systems: live SaaS products, production Claude agents, and code-mode MCP servers that operate enterprise infrastructure safely.
I was building agentic AI systems before most people had a name for them — autonomous tools that SSH into switches, classify devices, resolve tickets. The gap was obvious: AI developers didn’t understand infrastructure, and infrastructure engineers hadn’t touched AI.
Enterprise Infrastructure
Enterprise networks across many verticals. More than 12 years in technical pre-sales, learning that production systems require more than technical specifications — they demand reliability, security, and operational excellence. CISSP, CCNP, KCNA, and multiple wireless certifications while specializing in enterprise wireless architecture and zero-trust security.
Adding AI to the Stack
After completing Stanford's Machine Learning specialization, I built my first AI agent for network automation using Claude. RAG systems, autonomous agents, and agentic workflows that solve real infrastructure challenges — combining deep infrastructure knowledge with AI capabilities.
Open Source & Community
Selected tools shared as open source on GitHub: autonomous agents for support automation, RAG-powered knowledge systems, and zero-touch provisioning platforms. Projects like ruckus-ztp and osticket-agent have gained community traction, validating demand at the intersection of infrastructure expertise and AI.
What's Next
Small models at the edge, multi-modal AI in production infrastructure, and agentic systems that operate across network, cloud, and security boundaries. The interesting problems are where AI meets real-world constraints — latency, reliability, and scale.