DevOps & cloud engineering, production-grade from day one

Your app already works — the real question is whether it survives real traffic, deploys without drama, and tells you the moment something breaks. I take applications to the cloud the right way: infrastructure as code, passwordless pipelines, autoscaling containers, and observability wired in from the start.

The gap between “it runs locally” and “it runs for thousands of users” is operational, not algorithmic: how it deploys, how it scales, how it's secured, and how you see inside it when something goes wrong. That's the work — on Azure primarily, with patterns that transfer to any cloud.

The proof is a full e-commerce microservices platform I built end to end on Azure Container Apps: four services defined entirely in Terraform, shipped by a passwordless GitHub Actions pipeline on every push, talking over a private network, caching in Redis, load-levelling through an Azure Service Bus queue, autoscaling from zero with KEDA, and emitting distributed traces to Application Insights — with no password stored anywhere.

Whether you're launching a first product or hardening an existing one, the goal is the same: a system your team can operate confidently, roll back in seconds, and scale without a rewrite.

What you get

  • Infrastructure as code with Terraform — your entire cloud in version control, with a reviewable plan before every change
  • CI/CD pipelines with passwordless OIDC auth — build, test, and deploy on every push, no secrets stored in the repo
  • Containerized services on Azure Container Apps or Kubernetes (AKS), with KEDA autoscaling and scale-to-zero
  • Private networking, managed identity, and least-privilege access — no passwords, admin logins disabled
  • Async messaging and caching — Service Bus queues and Redis for load-levelling and resilience under spikes
  • Observability wired in — OpenTelemetry tracing, dashboards, and alerting on Azure Monitor and Application Insights
  • Zero-downtime deploys and instant rollback via immutable, git-SHA-tagged image revisions

How an engagement runs

  1. Assess and architect

    Map what you have and what “production” must mean for it — scaling targets, security posture, budget — then design the cloud architecture and write it down before a single resource is created.

  2. Codify the infrastructure

    Everything becomes Terraform with remote, locked state: no click-ops, no snowflake environments, and a reviewable plan for every change.

  3. Wire the pipeline

    A passwordless CI/CD pipeline builds, tags, and deploys on every push — with zero-downtime revisions and one-command rollback.

  4. Make it observable, then hand it over

    Tracing, dashboards, and alerts go in before launch, followed by docs and a walkthrough so your team can operate it without me.

Proof, not promises

Common questions

Which cloud do you work with?
Azure primarily — Container Apps, AKS, Terraform, and Azure Monitor — but the patterns (infrastructure as code, OIDC CI/CD, autoscaling containers, observability) transfer directly to AWS or GCP. The principles matter more than the logos.
Do I need Kubernetes?
Usually not to start. Azure Container Apps gives you autoscaling, scale-to-zero, and zero-downtime revisions with a fraction of the operational weight of Kubernetes. I move workloads to AKS when the control it buys is genuinely needed — not by default.
Can you work with my existing app?
Yes. The first step is a read-through of what's load-bearing, then I containerize it, put its infrastructure into Terraform, and build the pipeline around it — incrementally, without a big-bang rewrite.
What does “passwordless” actually mean?
No long-lived credentials stored anywhere. CI logs into the cloud with short-lived OIDC-federated tokens, services pull images and reach data with managed identities, and message queues use least-privilege keys. There's nothing sensitive in the repo to leak.