Job Title: AI DevOps Engineer
Rate: £525 (Inside IR35)
Duration: 12 Months
Location: Remote
Clearance: Active SC
Stages: 1 Stage

Key Responsibilities

• Design, build and maintain cloud-native infrastructure supporting AI and Machine Learning platforms.
• Develop and manage Infrastructure as Code (IaC) using Terraform, Bicep or ARM templates.
• Build, maintain and optimise CI/CD pipelines for AI, data and application deployments.
• Implement DevOps and MLOps best practices to automate model deployment, monitoring, retraining and life cycle management.
• Deploy and support containerised applications using Docker and Kubernetes.
• Automate cloud infrastructure provisioning across Azure and/or AWS environments.
• Configure monitoring, logging and alerting solutions to ensure high availability and operational resilience.
• Collaborate with Data Scientists, AI Engineers, Platform Engineers and Solution Architects to deliver production-ready AI solutions.
• Implement security controls, identity management and secrets management aligned with Government security policies.
• Support AI model governance through deployment automation, version control and auditability.
• Optimise platform performance, scalability and cost across cloud services.
• Develop automated testing, release management and rollback strategies.
• Troubleshoot infrastructure, deployment and platform issues across development, test and production environments.
• Contribute to technical documentation, knowledge sharing and engineering best practices.

Essential Skills & Experience

• Strong commercial experience as a DevOps, Platform or Cloud Engineer.
• Experience supporting AI, Machine Learning or Data Engineering platforms.
• Strong knowledge of Azure and/or AWS cloud services.
• Experience with Infrastructure as Code using Terraform, Bicep, ARM Templates or CloudFormation.
• Experience with Azure DevOps, GitHub Actions or Jenkins.
• Strong Kubernetes and Docker experience.
• Experience implementing CI/CD pipelines for cloud-native applications.
• Experience supporting container orchestration platforms.
• Knowledge of MLOps principles and AI model deployment.
• Experience with Git version control.
• Strong Linux administration skills.
• Experience implementing monitoring and observability using tools such as Azure Monitor, Prometheus, Grafana, ELK or Splunk.
• Experience managing secrets and identities using Azure Key Vault or AWS Secrets Manager.
• Knowledge of networking, IAM, RBAC and cloud security.
• Experience working within Agile and DevSecOps environments.