DevOps Engineer

A growing AI-focused business, the organisation develops cloud-native products and is strengthening its platform function to support product development and launches. Its platform team builds and maintains the infrastructure and systems that underpin these products, with a focus on Kubernetes, observability, security and the reliability of production environments.

Its stated priorities include a high technical bar, strong ownership, flexibility and forward-thinking use of AI in engineering. As the business scales, it is looking for people who bring practical experience from environments that have already tackled comparable growth and can help build a robust, dependable platform.

As Dev Ops Engineer, you will build, maintain and evolve the infrastructure that supports the organisation’s products. This is a hands-on, generalist position in which you will work with infrastructure as code and Kubernetes, improve existing systems and help shape the platform as product needs develop.

You will strengthen observability across both the products and their underlying infrastructure, and support software development teams as they launch and enhance features. You will also apply sound judgement to security and production risk: helping implement systems safely, creating or improving guardrails, and explaining how to identify and correct unsafe practices.

Working closely with platform and software development colleagues, you will take ownership of reliable production infrastructure and contribute to a growing function. Experience scaling systems beyond the organisation’s current stage will be particularly valuable.

• Hands-on infrastructure-as-code experience, ideally with Terraform or an equivalent.

• Strong, practical Kubernetes experience and familiarity with cloud-native applications;
  Azure is preferred, while AWS experience is relevant.

• Experience scaling systems beyond their current stage and taking responsibility for safe, reliable production infrastructure.

• A sound understanding of security, observability and production risk, alongside clear communication, adaptability, ownership and a forward-thinking approach to applying AI in engineering.