Senior MLOps Platform Engineer – On-site

Lorien; England; On-site; GBP 110,000 - 150,000; Full time

Job summary

Lorien seeks a Lead Platform Engineer to design, build and operate an MLOps platform on Kubernetes. This senior, hands-on technical leadership role focuses on deploying scalable ML and LLM workloads while working closely with data scientists and ML engineers.

You’ll own operability, reliability, security, and production support, ensuring a usable, well-documented platform that meets real production needs.

Qualifications

• Senior or Lead Platform Engineer with hands-on DevOps experience.
• Experience building Kubernetes-based platforms.
• Proficiency with Helm and Infrastructure as Code (Terraform).
• Experience extending Kubernetes with higher-level platforms and services.
• Strong ops fundamentals: monitoring, logging, incident response, reliability.
• Ability to work with data scientists to support production workloads.

Responsibilities

• Provide technical leadership across platform, DevOps, and MLOps.
• Design, build, and operate a Kubernetes-based MLOps platform.
• Implement and run MLOps tooling enabling experiments, packaging, deployment, and scalable inference.
• Build and operate model serving and inference platforms within Kubernetes.
• Work with data scientists and ML engineers to ensure usability and proper documentation.
• Own platform operability, reliability, security, and support in production.
• Troubleshoot complex issues across Kubernetes, platform services, and MLOps layers.
• Contribute to architectural decisions while remaining hands-on with delivery.
• Apply pragmatic engineering judgement in AI workloads.

Skills

Kubernetes

MLOps

DevOps

Helm

Terraform

Platform engineering

Leadership

Data scientist collaboration

Tools

Kubeflow

KServe

vLLM

JupyterHub

Kubernetes extensions

Terraform

Helm

Job description

Lorien seeks a Lead Platform Engineer to design, build and operate an MLOps platform on Kubernetes. This senior, hands-on technical leadership role focuses on deploying scalable ML and LLM workloads while working closely with data scientists and ML engineers.

You’ll own operability, reliability, security, and production support, ensuring a usable, well-documented platform that meets real production needs.