DevOps Engineer

DevOps / MLOps Engineer | AWS | CI/CD | AI/ML Infrastructure

About the role

We are looking for a strong DevOps / MLOps Engineer to own and evolve the infrastructure, automation and deployment practices supporting AI/ML models and applications.

This role sits at the intersection of DevOps, cloud infrastructure and MLOps. The ideal candidate has deep hands-on experience building robust CI/CD systems, managing AWS infrastructure and taking machine learning models from development through controlled production deployment and monitoring.

What you'll do

• Assess, improve and extend existing CI/CD pipelines, increasing reliability, consistency and coverage
• Enhance automation frameworks and repository templates used to build, validate and deploy packages, applications and ML models
• Own controlled promotion across development and production environments, including approval gates and rollback strategies
• Manage cloud services underpinning the AI/ML and application estate, including:
• Identity and access architecture
• Federated authentication from source control
• Artifact and container registries
• Compute and model-serving infrastructure
• Maintain build infrastructure, including self-hosted runners with access to accelerated compute
• Own the ML model lifecycle, including:
• Model registry and versioning
• Packaging models for inference
• Promotion criteria
• Production deployment
• Monitoring of deployed models
• Define container image build standards and embed image scanning and vulnerability gates into CI/CD pipelines
• Maintain and improve Infrastructure as Code, ensuring environment configuration remains consistent and in parity
• Contribute to data management and governance standards supporting AI/ML readiness

What we're looking for

• Strong, hands-on DevOps and CI/CD experience
• Deep experience with source-control-native CI/CD tooling, particularly GitHub Actions or equivalent
• Experience designing reusable, modular and templated workflows
• Strong AWS experience, including:
• IAM
• Cross-account architecture
• OIDC federation
• ECR
• Container compute
• Serverless compute
• Strong Infrastructure as Code experience using Terraform or AWS CDK
• Proven MLOps experience across the full model lifecycle, from model registry through production serving and monitoring
• Experience building and maintaining containerized deployment pipelines
• Strong understanding of software supply-chain security, including container scanning and vulnerability gating
• Ability to work independently and take ownership of infrastructure end-to-end

Nice to have

• Experience connecting on-premises AI/ML training infrastructure with cloud-based model serving
• Experience supporting AI/ML platforms in a regulated or enterprise engineering environment
• Experience with cloud cost management and capacity governance
• Experience working with accelerated or GPU-based compute infrastructure