ML Platform / DevOps Engineer

Role Overview

The ML Platform / DevOps Engineer will develop, secure and maintain cloud infrastructure and enable large-scale training, inference and evaluation platforms. Supporting the research and engineering teams by developing environments and automation pipelines necessary for teams to rapidly prototype, perform research, and continuous shipping of software. This role combines strong software engineering with hands-on cloud and DevOps experience and willingness to enable teams to move quickly while maintaining the security, governance and operational foundations needed to operate reliably at scale.

Role scope will be calibrated to the experience and profile of the candidate.

Key Responsibilities

Engineering

• Build and automate development environments that allow teams to safely experiment with AI, data and application development.

• Design and implement CI/CD pipelines and deployment patterns, particularly using GitHub and GitHub Actions.

• Develop reusable infrastructure templates, provisioning workflows and automation for rapid and consistent environment creation.

• Establish environment access controls, governance and operational guardrails across development and production environments.

• Build and maintain integrations between GitHub, cloud services and supporting technology platforms.

• Establish DevSecOps practices, embedding appropriate security, compliance and operational controls into platform delivery.

• Develop standards and reusable patterns that can be adopted across multiple development teams.

Collaboration

• Work with engineering and research teams to onboard testing environments; streamline release and provide compute and data scaling for on-going and ad-hoc research projects.

• Advise development teams on platform architecture, deployment, scalability, operational support and long-term maintainability.

• Improve developer workflows and establish practices that enable teams to move rapidly from experimentation through to production.

• Contribute to a rigorous and supportive engineering culture, sharing knowledge and helping establish effective platform engineering practices.

Essential

• Strong experience in ML platform engineering, DevOps.

• Strong experience working with AI/ML platforms at scale (training/serving).

• Strong experience with GitHub and GitHub Actions, including CI/CD and automated deployment.

• Hands-on experience with cloud platforms, ideally AWS or GCP.

• Experience with Infrastructure as Code and infrastructure automation (i.e. Terraform, Pulumi).