IT & Software

Lead Platform Engineer

Lorien Resourcing

London · Greater London · United Kingdom

Overview

In this senior, hands-on leadership role you will design, build, and operate a Kubernetes-based MLOps platform powering AI/ML production workloads. You lead through technical depth and delivery, enabling data scientists and ML engineers to experiment, deploy, and run models safely at scale. The role focuses on scalable, usable, and secure platform foundations layered on Kubernetes to support real-world workflows. You’ll work closely with cross-functional teams to ensure reliability and operability in production.

Responsibilities
  • Provide technical leadership across platform, DevOps, and MLOps
  • Design, build, and operate a Kubernetes-based MLOps platform supporting the full model lifecycle
  • Implement and run MLOps tooling for model experimentation, packaging, deployment, and scalable inference
  • Build and operate model serving and inference platforms within Kubernetes environments
  • Collaborate with data scientists/ML engineers to ensure usability, documentation, and workflow alignment
  • Own platform operability, reliability, security, and supportability in production
  • Troubleshoot complex issues across Kubernetes, platform services, and MLOps layers
  • Contribute to architectural decisions while remaining hands-on in delivery
  • Apply pragmatic engineering judgement under operational AI workloads
Key requirements
  • Senior or Lead Platform Engineer / DevOps Engineer experience
  • Deep hands-on experience building and operating Kubernetes-based platforms
  • Strong experience with Helm and Infrastructure as Code (Terraform)
  • Proven ability to extend Kubernetes with higher-level platforms and services
  • Strong understanding of monitoring, logging, incident response, reliability, and maintenance
  • Experience supporting production workloads with engineers and data scientists
  • MLOps experience (not just research) with tools like Kubeflow or similar
  • Experience running model serving/inference platforms (Kserve, vLLM, or equivalent)
  • Notebook-based environments such as JupyterHub in secure platforms
  • Exposure to emerging AI tooling (InstructLab, Trustworthy/Responsible AI tools)
  • Collaborative cross-functional teamwork
  • Pragmatic engineering judgement
  • Problem-solving under high operational demand
  • Kubernetes
  • Helm
  • Terraform

Reference: WJ-747_30165499

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