ML Ops Infrastructure Engineer (UK), TWG Global AI, GB

At TWG Group Holdings, LLC ("TWG Global"), we drive innovation and business transformation across a range of industries, including financial services, insurance, technology, media, and sports, by leveraging data and AI as core assets. Our AI-first, cloud-native approach delivers real-time intelligence and interactive business applications, empowering informed decision-making for both customers and employees.

We prioritize responsible data and AI practices, ensuring ethical standards and regulatory compliance. Our decentralized structure enables each business unit to operate autonomously, supported by a central AI Solutions Group, while strategic partnerships with leading data and AI vendors fuel game-changing efforts in marketing, operations, and product development.

You will collaborate with management to advance our data and analytics transformation, enhance productivity, and enable agile, data-driven decisions. By leveraging relationships with top tech startups and universities, you will help create competitive advantages and drive enterprise innovation.

At TWG Global, your contributions will support our goal of sustained growth and superior returns, as we deliver rare value and impact across our businesses.

The Role:

We are looking for an ML Ops / Infrastructure Engineer to build and scale the pipelines, platforms, and tooling that power TWG Global's AI-first future. As an MLOps/Infra Engineer, your focus will be on the systems and automation that make ML applications production-ready, reliable, and cost-efficient at scale. You'll design infrastructure that accelerates model development, ensures secure and compliant deployment, and enables continuous monitoring across a regulated enterprise environment.

Responsibilities:

• Architect and maintain ML infrastructure: data pipelines, feature stores, model registries, and distributed training systems.
• Build and manage automated CI/CD pipelines for ML, ensuring reproducibility, rollback, and seamless deployment.
• Implement observability frameworks: monitoring, logging, and alerting for data drift, model accuracy, latency, and infrastructure cost.
• Design and manage containerized, Kubernetes-based deployments for scalable inference and real-time AI applications.
• Integrate cloud services, APIs, and infrastructure with TWG's broader data and engineering ecosystem.
• Embed controls, audit trails, and governance into ML pipelines to meet compliance and regulatory requirements.
• Partner with Data Scientists to operationalize models, and with ML Engineers to ensure systems are robust, scalable, and efficient.
• Continuously automate workflows and optimize cost-to-serve, reducing the time from experimentation to production.