Building Platforms at Scale
// where infrastructure meets impact
I build the platforms that engineering teams ship on. At Abridge AI, I helped grow the platform engineering organization from 1 to 25, standing up the cloud infrastructure, Kubernetes clusters, and CI/CD pipelines that let a healthcare AI company move fast without breaking compliance.
Over 12+ years, I've focused on the intersection of developer experience and production reliability—designing systems where "it just works" is the result of a lot of deliberate engineering underneath. Cloud-native architectures, infrastructure as code, and a philosophy that the best platform is the one developers don't have to think about.
What sets me apart is the breadth behind the depth. The hobby-tech—game engines, ML pipelines, systems programming—isn't a distraction. It's the reason I can debug a kernel panic, reason about GPU memory, or intuit why a distributed system is misbehaving. Curiosity is the cheat code.
What I Work With
// the toolkit, abridged
Cloud & Platform Engineering
Designing the foundations teams build on
Systems Programming
Close to the metal, far from the crashes
AI/ML & Computer Vision
Teaching machines to see and think
Game Dev & Real-time
Where latency budgets are measured in frames
Full-Stack Web
From database to deploy button
Infrastructure & Homelab
Production-grade infra, closet-grade budget
Data & Optimization
Making numbers tell the truth, faster
Beyond the Day Job
// where curiosity meets compilers
Real-time Networked Systems
Building game servers taught me more about distributed systems than any textbook. When you need state synchronization across dozens of clients at 60Hz, you learn to think about consistency, latency budgets, and graceful degradation in ways that translate directly to production platform work.
AI at the Edge
Running computer vision models and LLM inference on local GPUs means understanding the full stack—from CUDA memory management to model optimization to serving infrastructure. These patterns map cleanly to building ML platforms at scale.
Systems from Scratch
Writing in Rust and Go for fun builds the kind of intuition that makes you dangerous in a production debugging session. When you've fought the borrow checker and hand-managed goroutine lifecycles, reasoning about memory leaks and race conditions in production becomes second nature.
The Homelab
A self-hosted infrastructure rack is a low-stakes production environment. TrueNAS clusters, ZFS storage pools, network segmentation, monitoring stacks—it's the same discipline as cloud platform engineering, just with more trips to the closet.
Let's Talk
// open channels
Whether it's platform engineering, a Rust-vs-Go debate, or something else entirely—I'm always up for a good conversation.