Senior Platform Engineer

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.

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 experimentation
  
  Model packaging, deployment, and promotion
  
  Scalable 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

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 Dev Ops 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

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 Jupyter Hub
• 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