AI Platform Engineer

Role Overview

We are looking for a highly hands-on DevOps / Platform Engineer to design, build, and manage the organisation's container and cloud platform.

The successful candidate will have direct experience building, provisioning, configuring, upgrading, securing, and operating Kubernetes clusters in production environments.

You will be responsible for the underlying platform, including cluster architecture, networking, storage, security, governance, observability, automation, and reliability. Exposure to AI infrastructure, MLOps, GPU-enabled environments, or the deployment of AI workloads would be advantageous.

Key Responsibilities

• Design and build Kubernetes clusters across development, testing, staging, and production environments.

• Provision and configure Kubernetes clusters using managed services or self-managed distributions.

• Own the full Kubernetes cluster lifecycle, including installation, configuration, scaling, patching, version upgrades, backup, recovery, and decommissioning.

• Manage Kubernetes control-plane and worker-node architecture, cluster capacity, availability, and performance.

• Configure and maintain cluster networking, ingress controllers, DNS, load balancing, service discovery, storage classes, and persistent volumes.

• Implement Kubernetes security controls, including RBAC, secrets management, network policies, pod security standards, image security, and admission controls.

• Build and maintain reusable container platform services for engineering and application teams.

• Develop automated cluster-provisioning and configuration-management processes using Infrastructure as Code.

• Establish platform governance standards, security baselines, naming conventions, deployment policies, and production-readiness requirements.

• Define and maintain reusable templates, Helm charts, platform blueprints, and golden paths.

• Build and manage CI/CD and GitOps capabilities using tools such as GitHub Actions, GitLab CI, Jenkins, Argo CD, or Flux.

• Manage container registries, image lifecycle processes, vulnerability scanning, and software-supply-chain controls.

• Partner with security, infrastructure, architecture, and engineering teams to ensure the platform meets governance and compliance requirements.

• Support production incidents and drive root-cause analysis and long-term remediation.

• Evaluate and introduce new platform technologies that improve scalability, security, reliability, and developer productivity.

• Support AI and machine-learning workloads, including GPU-based clusters, containerised model deployment, model-serving platforms, and MLOps pipelines where applicable.

Requirements

• Strong hands-on experience in DevOps, platform engineering, cloud infrastructure, or site reliability engineering.

• Proven experience building and managing Kubernetes clusters, rather than only deploying applications onto existing clusters.

• Experience owning Kubernetes clusters through their full operational lifecycle.

• Experience with Infrastructure as Code tools such as Terraform, Pulumi, CloudFormation, or similar technologies.

• Experience with configuration-management and automation tools such as Ansible.

• Experience with Helm, Kubernetes Operators, GitOps, and automated cluster deployment.

• Strong knowledge of Linux administration, networking, infrastructure security, identity and access management, and production operations.

• Experience defining governance controls, platform standards, security baselines, and operational policies.

• Familiarity with observability tools such as Prometheus, Grafana, ELK, OpenSearch, Datadog, or Splunk.