DevOps & platform engineering

Platform engineering for product teams, without hiring a platform team.

We design, build and run cloud infrastructure for product companies: AWS and Kubernetes, GPU capacity for AI workloads, delivery pipelines, cost, security and compliance, observability. More than fifteen years of hands-on work, remote from Bucharest, for teams across the UK and EU.

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What we work with every day
  • AWS
  • Azure
  • Kubernetes
  • Terraform
  • OpenTofu
  • Helm
  • GitHub Actions
  • Azure DevOps
  • GitLab
  • HashiCorp Vault
  • NVIDIA GPUs
  • vLLM
  • Prometheus
  • Grafana
  • Aurora PostgreSQL
15+ years across infrastructure, SRE, DevOps and engineering leadership
CKA · CKAD · CKS Kubernetes certified, plus HashiCorp Terraform and Vault, and GitHub Actions
10+ years on AWS from managed hosting and EC2 to GPU-enabled EKS platforms for AI products
Platform · Tooling · Release engineering teams led as a manager at a large consultancy, while staying hands-on

What we do

The work a platform team would do, delivered by someone who has run it in production, been on call for it, and led the team doing it.

Kubernetes, managed or your own

EKS, AKS, OKE and other managed clusters, or self-managed ones with Rancher. Upgrades without downtime, node pools and autoscaling with Karpenter or Cluster Autoscaler, Helm and GitOps, fleet management with tools like Rafay.

CI/CD platforms

GitHub Actions, Azure DevOps or GitLab, self-hosted runners where they pay off, caches that make builds fast, deploy pipelines with gates and rollbacks, and an end to flaky end-to-end suites.

Cloud cost

Spend audits with attribution per service, savings plans and right-sizing, NAT and egress analysis, and removal of what nobody uses. Findings first, then the changes.

Security and compliance

The infrastructure side of ISO 27001 and Cyber Essentials with Vanta or Drata, CDN and WAF in front of public endpoints, secrets in Vault, image and CVE hygiene, and answers for customer security questionnaires.

Observability and on-call

Prometheus, Grafana and Loki set up properly, alerts tuned so they page only when it matters, incident playbooks, and the fixes that stop the same incident coming back.

AI infrastructure

GPU-enabled EKS with NVIDIA tooling and scale-from-zero node pools, self-hosted vLLM inference, LLM agents as production workloads, and coding agents that operate CI and infrastructure under review.

How we work

Three ways to engage. All of them end with your team able to run what we built.

Fractional platform engineer

A fixed number of days a month inside your team: your Slack, your repos, your incidents. We own the platform backlog so your developers can keep shipping.

Fixed-scope projects

An EKS upgrade, a CI migration, a cost audit, an ISO 27001 push. Scoped and priced up front, delivered with the runbooks to keep it running.

Reviews and second opinions

A close look at your architecture, pipelines or cloud bill, with a written report and a prioritised list of what to change first.

  • Production experience, not slide decksEverything we recommend we have run ourselves, in production, with a pager.
  • Written downDecisions, runbooks and findings land in your wiki and your repos, not in our heads.
  • Boring on purposeFewer moving parts, rehearsed upgrades, alerts that mean something.

Latest writing

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Rebuilding this site with Claude Code, still free on GitHub Pages

How a stale Hugo blog became a firm site in one evening with Claude Code: what the agent did, the pain points it found and fixed, the GitHub Pages deploy that …

AI Platform Engineering: what changes when agents become your platform's users

Notes from running an LLM agent as a production workload on Kubernetes and letting coding agents operate the platform: what stayed the same, what changed, and …

Retiring WordPress: a byte-identical migration to Hugo and GitHub Pages

How we moved a WordPress site off AWS onto Hugo and GitHub Pages without visitors noticing — capture, verify byte-for-byte, cut over DNS, and stop paying for an …

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