ML Platform Engineer

ML Platform Engineer
Remote 6 month contract

This is an opportunity to join a high-impact AI platform project, helping to build and scale the infrastructure that enables machine learning models to be deployed, monitored, and operated in production. You'll work across MLOps, Kubernetes, GPU inference, and modern AI tooling, contributing to a platform that supports the full machine learning lifecycle.

The Company

They are a technology-focused organisation investing in machine learning and AI platform capabilities. Their engineering teams are building scalable infrastructure that enables rapid experimentation, reliable model deployment, and efficient operational workflows. The environment is collaborative, technically driven, and focused on delivering reusable solutions that improve productivity across engineering and machine learning teams.

The Role and Deliverables

• Build and enhance ML platform infrastructure supporting the end-to-end machine learning lifecycle.
• Design and implement deployment workflows for machine learning models in cloud-native environments.
• Develop and maintain CI/CD pipelines for ML artefacts and production deployments.
• Support GPU inference workloads using technologies such as NVIDIA Triton and KServe.
• Build observability, monitoring, and metrics tooling for production services and microservices.
• Enable scalable Kubernetes-based infrastructure, including cluster management and automation.

Your Skills & Experience

• Strong experience working with Kubernetes in production environments.
• Strong machine learning, MLOps, or ML platform engineering background.
• Experience building and maintaining CI/CD pipelines.
• Track record deploying and operating machine learning models at scale.
• Hands-on experience with NVIDIA Triton, KServe, or similar model serving technologies.
• Understanding of GPU orchestration and inference infrastructure.
• Exposure to agentic AI coding systems or AI-assisted developer tooling.
• Ability to work autonomously, ask insightful technical questions, and operate effectively in fast-moving environments.
• Gaming industry experience is advantageous but not essential.