Lead Platform Engineer

Lead Platform Engineer

Building the platforms that make AI and machine learning work in production

We're looking for a Lead Platform Engineer to join a growing engineering organisation and play a pivotal role in designing, building, and operating an MLOps platform that enables AI and data science teams to deliver reliably in production.

This is a senior, hands‑on technical leadership role, not a people‑management position. You'll lead through technical depth, judgement, and delivery, building the tooling, workflows, and operational foundations that allow data scientists and ML engineers to experiment, deploy, and run ML and LLM‑based workloads safely and at scale.

The focus is not simply on running Kubernetes clusters - it's on layering real MLOps capability on top of Kubernetes to create a platform that is usable, supportable, and trusted in live environments.

What you'll be doing

You'll act as a technical leader across platform engineering, DevOps, and MLOps, remaining deeply involved in implementation.

You will:

• Provide technical leadership across platform, DevOps, and MLOps activities
• Design, build, and operate a Kubernetes‑based MLOps platform supporting the full model lifecycle
• Implement and runMLOps tooling that enables teams to:
  • Experiment with models and notebooks
  • Package, version, and deploy models
  • Run scalable inference and LLM‑based workloads
• Build and operate model serving and inference platforms within Kubernetes environments
• Work closely with data scientists and ML engineers to ensure the platform is usable, well‑documented, and aligned to real workflows
• Own platform operability, reliability, security, and supportability in production
• Troubleshoot complex issues across Kubernetes, platform services, and MLOps layers
• Contribute to architectural decisions while staying hands‑on with delivery
• Apply pragmatic engineering judgement in environments where AI workloads place real operational demands on infrastructure

What we're looking for

This role suits someone who is fundamentally a strong platform engineer, with the depth to apply those skills confidently to MLOps.

Essential experience:

• Strong background as a Senior or Lead Platform Engineer / DevOps Engineer
• Deep, hands‑on experience building and operating Kubernetes‑based platforms
• Strong practical experience with Helm and Infrastructure as Code (e.g. Terraform)
• Proven experience extending Kubernetes with higher‑level platforms and services, not treating it as the finished product
• Strong understanding of operational fundamentals: monitoring, logging, incident response, reliability, and maintenance
• Comfortable working directly with engineers and data scientists to support real production workloads

MLOps experience (key to the role)

You'll work deeply "in the weeds" of MLOps platforms, enabling ML and LLM workloads (not model research).

Experience in areas such as:

• Building or operating MLOps platforms using tools like Kubeflow or similar frameworks
• Running model serving and inference platforms (e.g. KServe, vLLM, or equivalent)
• Supporting LLM‑based workloads, including optimisation and serving considerations
• Providing notebook‑based environments such as JupyterHub in secure platforms
• Exposure to emerging tooling such as InstructLab, Trustworthy / Responsible AI tooling, or comparable solutions

Desirable experience

• Building internal platforms specifically for data science and ML teams
• Operating AI‑enabled or data‑driven systems in production
• Experience in regulated, security‑conscious, or high‑assurance environments
• Designing platforms that balance user flexibility with governance and control

If you believe Kubernetes is the base, not the product, enjoy operating close to the metal, and like solving hard platform problems that enable others to succeed, this role offers real challenge and impact. If interested, apply now!