The role centres on building and running the cloud setup that lets researchers and engineers try ideas and ship software safely. A solid candidate has hands-on Dev Ops and ML platform experience, especially with Git Hub Actions pipelines, Terraform or Pulumi, containers, Kubernetes, and AWS or GCP.

They also bring a sound grasp of access controls and security, plus the software skills to build reusable self-service tooling. The posting says scope will be matched to the person, so seniority is not fixed.

How it fits the AI era

AI makes the training, serving and testing workloads heavier and changes quickly, so the platform has to scale compute and data on demand while keeping guardrails in place. Agentic tools can speed up routine pipeline and knowledge-sharing work, but deciding on architecture, access rules and secure defaults stays with people.

Trust in the platform comes from careful judgment about what teams and agents are allowed to do.

How it builds AI experience





This job offers close contact with research teams working on a system that controls what AI agents can do, which is a helpful place to learn how AI workloads are run and governed. Someone could build the same experience by setting up a small model training and serving pipeline on AWS or GCP, with automated deployment and strict permissions.

Trying agent-based automation inside a code hosting and messaging workflow would also match the preferred skills.

Worth showing
Git Hub Actions CI/CD pipelines
Kubernetes and container deployments
Cloud access control and security

This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.

About Metacognition AI

An operating system for AI agents that sits beneath language models to control what they can do, released as open source.

📌 Ml Platform / Devops Engineer Newcastle
🏢 present of AI
📍 Newcastle