I’m a Senior Platform Engineer at Johnson Controls International and a Computer Science student at Arizona State University. I started at JCI as an intern and worked my way up — my work now focuses on cloud-native infrastructure and AI-powered operations, building the tooling and architecture that keeps distributed systems observable, reliable, and intelligent.
I grew up on the command line, starting with a used Tandy 286 running MS-DOS in the early 90s. Growing up alongside the evolution of modern computing gave me a deep intuition for how systems work at every level, and that perspective still drives how I approach infrastructure today.
I design and maintain large-scale microservice architectures on Azure Cloud using AKS and FluxCD. My day-to-day involves deployment strategy, service reliability, and building operational foundations that let teams ship with confidence. I work primarily in Go, Python, PowerShell, and Bash, with Terraform for infrastructure-as-code.
I’m particularly interested in bringing AI capabilities directly into infrastructure operations. I work with the Model Context Protocol (MCP) to build diagnostic tooling that gives AI models structured access to real-time cluster and resource state, and I’ve been developing approaches to AI observability — using MCP-based architectures as a control plane for cloud resources. I also lead GitHub Copilot adoption and governance within my organization.
I’m completing my BS in Computer Science at Arizona State University, where my coursework has covered computer architecture, algorithms, and computational theory.
I’m always open to conversations about platform engineering, AI-powered operations, or MCP tooling. Feel free to reach out.