Senior AI Platform Engineer — Kubernetes & MLOps

Lorien; England; On-site; GBP 90,000 - 120,000; Full time

Job summary

Lorien is seeking a Senior Platform Engineer to design and operate a Kubernetes-based platform that powers AI/ML delivery. You will create infra, tooling, workflows and guardrails so multiple ML and data teams can deploy and run models safely at scale.

The role emphasizes hands-on engineering, collaboration with MLOps and data scientists, and responsibility for platform reliability, security, and lifecycle in production.

Qualifications

• Strong background as a Senior Platform Engineer or Senior DevOps Engineer.
• Hands-on experience building and operating Kubernetes-based platforms.
• Experience with Helm and Infrastructure as Code (Terraform).
• Proven track record building internal platforms for other engineers.
• Strong grasp of monitoring, reliability, incidents, and maintainability.
• Collaborating with MLOps engineers and data scientists.

Responsibilities

• Design, build, and operate a Kubernetes-based platform that supports multiple ML and engineering teams.
• Extend Kubernetes with MLOps capabilities to go beyond a finished product.
• Provide platform-level support for model development, packaging, deployment, and promotion.
• Build shared platform services to enable consistent, repeatable model deployment.
• Work closely with data scientists and MLOps engineers to ensure platform usability and fit for purpose.
• Own platform operability, reliability, security, and lifecycle management in production.
• Troubleshoot complex issues across infrastructure, Kubernetes, and MLOps layers.
• Contribute to architectural decisions while remaining hands-on with implementation.

Skills

Kubernetes

Platform engineering

DevOps

MLOps collaboration

Monitoring

Tools

Helm

Terraform

Job description

Lorien is seeking a Senior Platform Engineer to design and operate a Kubernetes-based platform that powers AI/ML delivery. You will create infra, tooling, workflows and guardrails so multiple ML and data teams can deploy and run models safely at scale.

The role emphasizes hands-on engineering, collaboration with MLOps and data scientists, and responsibility for platform reliability, security, and lifecycle in production.