Two things. Done properly.

Kubernetes earns its place when a workload has to scale on a real signal, not when one service would do. On Amazon EKS we run the control plane, node groups, ingress and the application manifests, and we review those changes like any other pull request.

The published example is a fintech automation platform. Browser agents sat on StatefulSets, so adding capacity was manual and removing it could cut a session that was still running. We moved the fleet to Deployments, added KEDA so replica count followed queue depth, and ran UAT and production as two EKS clusters, with Prometheus and Grafana on queue latency rather than CPU alone. The same work removed a latest tag that let Kubernetes run a stale image. Read the Kubernetes migration and KEDA autoscaling case study.

On a retainer we stay for cluster reviews, the scaling signal, and on-call that pages someone only when the queue is actually late.

An AWS account that lives only in the console will drift. A port opens, a bucket policy changes, a database grows, and six months later nobody can say what production is supposed to be. We keep infrastructure in Terraform or Pulumi so a change is a diff someone can review, the account can be rebuilt, and a bad change has a way back. That is the same bar as the rest of the DevOps work on this page.

The published infrastructure engagement is the EKS platform case study: two clusters, manifests you can read, and deploys pinned to a commit rather than a floating tag. The banking workforce platform is the other half, inside a change-advisory process, where production access is time-limited and IAM and cost are reviewed instead of left to pile up. Neither write-up is a line-by-line Terraform module, and this page will not pretend that it is.

Putting the account in code is retainer work: reviewable, reproducible, and recoverable when a change goes wrong.

A pipeline is part of the product. It is how a commit becomes a release, how a bad release comes back, and, on a regulated team, part of the record of what ran. We build and repair that path on GitHub Actions, GitLab CI, Bitbucket Pipelines and Azure DevOps, including the gates between environments.

On a tier-1 bank's workforce platform the pipeline sits inside change control. Builds are traceable from commit to artifact, promotion from staging to UAT to production needs an explicit approval, and a change is checked for a rollback path before it reaches the change advisory board. That engagement is the banking workforce platform case study.

A fintech platform had a quieter failure. A latest tag meant Kubernetes sometimes ran a cached image instead of the build CI had just passed. Pinning every deploy to the commit SHA removed it. The write-up is the Kubernetes migration and KEDA autoscaling case study. On a retainer, release engineering is the pipeline, the gates, and a rollback you have actually tried.