Senior Platform Engineer

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

Senior Platform Engineer - DV Clearable - 5 Days On-Site

Build and operate the platforms that make AI and machine learning work at scale

We're looking for a Senior Platform Engineer to join our team and play a key role in designing and operating the platform that underpins AI and machine learning delivery.

This is a hands‑on senior platform role, focused on building robust, Kubernetes‑based platforms that enable MLOps engineers, ML engineers, and data scientists to deploy, run, and manage models safely and effectively in production.

While you'll need a strong understanding of how machine learning and LLM workloads are trained, packaged, deployed, and served, this is not a "deploy models all day" role. Instead, your impact will come from creating the infrastructure, tooling, workflows, and guardrails that allow others to do that work reliably and at scale.

What you'll be doing

You will:

• Design, build, and operate a Kubernetes‑based platform that supports multiple ML and engineering teams
• Extend Kubernetes with MLOps‑specific capabilities, rather than treating it as a finished product
• Provideplatform‑level support for:Model development and experimentationModel packaging, deployment, and promotionScalable inference and LLM‑based workloads
• Build shared platform services that enable consistent, repeatable model deployment, even where day‑to‑day deployment is owned by MLOps or ML engineers
• Work closely with data scientists and MLOps engineers to ensure the platform is genuinely usable and fit for purpose
• Own platform operability, reliability, security, and lifecycle management in production
• Troubleshoot complex issues that cut across infrastructure, Kubernetes, and MLOps layers
• Contribute to architectural decisions while remaining hands‑on with implementation

What we’re looking for

This role is ideal for someone who sees themselves first and foremost as a platform engineer, with the depth to support AI and ML workloads properly.

• Strong background as a Senior Platform Engineer or Senior DevOps Engineer
• Deep, hands‑on experience building and operating Kubernetes‑based platforms
• Strong practical experience with Helm and Infrastructure as Code (. Terraform)
• Proven experience building internal platforms for other engineers, not just running workloads
• Strong grasp of operational fundamentals: monitoring, logging, reliability, incidents, and maintainability
• Comfortable collaborating closely with MLOps engineers and data scientists, even where responsibilities differ

ML platform & MLOps knowledge (important)

You don't need to be a full‑time MLOps engineer - but you do need practical understanding of how ML and AI workloads behave in production.

Experience or exposure to areas such as:

• MLOps platforms (. Kubeflow or similar frameworks)
• Model serving and inference platforms (. KServe, vLLM, or equivalent)
• Supporting LLM‑based workloads, including performance and scaling considerations
• Notebook environments such as JupyterHub
• Awareness of emerging tooling around Responsible / Trustworthy AI or comparable solutions

This ensures you're building a platform that actually works for AI use cases - not a generic compute layer.

• Working in organisations with a clear AI or data platform strategy
• Supporting data scientists or ML engineers at scale
• Experience in regulated, secure, or high‑assurance environments
• Designing platforms that balance flexibility, governance, and control