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Platform engineering / AI infrastructure / operations

I build the layer other engineers depend on.

Enterprise production experience, internal developer platform delivery, and a privately operated cloud-native estate converge in one working style: make the platform legible, reusable, observable, and recoverable.

10+

years in production systems

9

k3s nodes operated

25+

GitOps applications

10

CNCF certifications

01 — Why the fit is real

Evidence across employment and independent builds.

Enterprise production

Python automation, Kubernetes integrations, observability migrations, and distributed-system diagnosis across complex customer environments.

Developer platforms

Hands-on adoption work across Kubernetes, CI/CD, identity, APIs, and cloud infrastructure for enterprise internal developer platforms.

Day-two operations

GitOps, networking, identity, secrets, state, metrics, logs, traces, recovery, and upgrades operated as one connected system.

AI infrastructure

Private vLLM inference, GPU capacity, retrieval, memory, agent tools, and the telemetry required to operate them safely.

03 — Interview walkthrough

Show the reasoning, not a feature tour.

Ask me to trace a failure across Kubernetes, identity, networking, storage, observability, and an AI workload. I can show the architecture, the evidence path, the recovery decision, and what changed afterward.

01

Start with the user-visible symptom

Establish impact before selecting a familiar layer to blame.

02

Follow evidence across boundaries

Trace network flow, workload state, storage, identity, and telemetry as one request path.

03

Restore, then explain

Separate containment from root correction and make the recovery path executable.

04

Change the operating system

Leave behind a guardrail, alert, runbook, or platform capability that reduces repeat work.

The short version

I do not stop at understanding the system. I ship the capability and stay close enough to learn whether it worked.

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