Senior Cloud Platform Engineer

The role

The Cloud Platform team builds and operates the shared infrastructure and developer tooling that every engineering team at Liberis depends on. We’re now running all new services in GCP and are actively migrating onto GCP from our legacy Azure platform. As we scale our use of AI, we’re looking to add a Senior Cloud Platform Engineer with an emphasis on AI Enablement to help build and run the shared infrastructure our product teams depend on and to expand how we use AI across the platform.

• Build and maintain the cloud infrastructure and tooling our product teams rely on, across compute, networking, CI/CD, and deployment on Kubernetes or Cloud Run.
• Champion AI agent adoption across the platform: spot where agents can take on real work, prototype it, and turn the promising work into supported, reusable capabilities teams can build on.
• Build the foundations for AI agents: integrations with Claude, the tools and services agents call, and the orchestration that ties them together.
• Wire observability, guardrails, and cost controls into the AI agents and tooling we run, so they stay safe and predictable in production.
• Work hands‑on with the AI tooling and agent frameworks we build on — Claude in particular, which we use extensively — and bring what you learn back to the wider team.
• Manage infrastructure with Terraform, keeping environments consistent, auditable, and reproducible.
• Contribute to our Azure‑to‑GCP migration thinking, especially what it means for our AI tooling and agents.
• Partner with product engineers to lower the barrier to using AI, writing the docs, templates, and examples that drive adoption.

• Genuine enthusiasm for AI agents and a desire to help us adopt them well. You don’t need a machine‑learning or model‑training background — this is about building with tools like Claude, not training or serving models — but you should be keen to learn and to lead by doing.
• Strong GCP experience (Azure experience is nice to have).
• Comfortable with containerisation (Docker) and deployment on Kubernetes or Cloud Run.
• Infrastructure‑as‑code with Terraform.
• Proficient in at least one of .NET (C#), Node.js, or Python.
• Observability experience with Datadog or cloud‑native logging and monitoring (GCP Cloud Operations or Azure Monitor).
• A track record of driving adoption — building things engineers use, writing docs that get read.
• Comfortable leading technical decisions, collaborating across teams, and mentoring other engineers.