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

ImplementandrunMLOps 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!

Guidant, Carbon60, Lorien & SRG - The Impellam Group Portfolio are acting as an Employment Business in relation to this vacancy.