About

The infrastructure work I'm proudest of looks more like software engineering than traditional ops; modular, version-controlled, built to be handed off without a knowledge-transfer session. That foundation comes from where I started: shipping AI-driven backend systems and NLP pipelines before moving fully into cloud and infrastructure automation.

I automated provisioning for 100+ network switches serving enterprise clients including Google and Morningstar, cutting deployment time by 50% through Python-driven configuration at scale. In previous roles, I've led zero-downtime migrations of hundreds of workloads and engineered cost optimization strategies that reclaimed six figures in annual cloud/licensing spend.

My stack centers on AWS, Terraform, and Kubernetes, with Python running through most of my automation work. My AI research background is a layer most infrastructure engineers don't carry - I've shipped production NLP pipelines and understand firsthand what the infrastructure beneath those systems actually demands.

I'm looking for infrastructure problems with real scale: platform engineering at the org level, multi-account architecture, or cloud strategy where the line between software and infrastructure is blurry by design.

Toronto has one of the most genuinely diverse food scenes I've eaten my way through. My version of exploring the city is a restaurant recommendation from a neighborhood I haven't tried yet, followed by a longer walk home to justify it.

At home, I run a lab that does what it sounds like: breaks, teaches me something, and gets rebuilt better. It's where infrastructure decisions have real consequences at 11pm with no on-call escalation path and stakeholders (my wife) keeping a watchful eye on you - which turns out to be a surprisingly effective way to learn.

I'm also deep in cloud certifications right now, less for the credential than for what structured study does: it forces the gaps in my mental model to the surface.