Platform Engineer building reliable Kubernetes platforms and developer tooling.

A Go-based self-service route-path visualization tool that made cross-account AWS networking issues faster to diagnose.

Problem

Cloud routing checks were manual, slow, and difficult for non-platform engineers to reason about. This created repeated support loops whenever teams needed to understand connectivity paths.

Outcome

Reduced network connectivity diagnosis time by roughly 90% and enabled engineers and product owners to answer routing questions with less direct platform-team support.

AI-assisted dependency discovery and diagram-as-code workflows for disaster recovery planning across hundreds of components.

Problem

Disaster recovery planning needed accurate service dependency maps, but manual discovery across hundreds of repositories and components would have been slow and error-prone.

Outcome

Mapped 200+ components and compressed discovery work into a shorter validation workflow, helping the DR initiative move from unknowns to actionable architecture review.

Python tooling that scanned Kubernetes namespace quotas, HPA limits, and Karpenter capacity before scale risks turned into incidents.

Problem

Namespace quotas could silently block workloads from scaling to HPA maximums during peak traffic. Existing checks did not give teams an actionable view before the risk mattered.

Outcome

Warned 45+ teams before peak-traffic risk became incidents and validated quota suggestions against Karpenter capacity limits.

Improved rollout observability, rollback behavior, and autoscaling validation around Helm, Argo Rollouts, and KEDA.

Problem

Canary releases, rollback workflows, and autoscaling configurations had edge cases that could confuse developers or make operational signals harder to trust.

Outcome

Made progressive delivery workflows safer, reduced developer context switching, and improved confidence in rollout and autoscaling behavior.