​Location: Remote - London

Type: Contract - 6 months rolling

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About the Role

We're looking for an ML Ops Engineer to join a leading energy company as part of the Wholesale Markets team. This role focuses on building the infrastructure and tooling to help data scientists turn research models into scalable, production-grade solutions.

The Wholesale Markets function sits at the core of the energy trading strategy. They leverage data and advanced analytics to forecast market movements, manage risk, optimise generation assets, and support energy procurement.

You'll work closely with the Tech Lead and support the full ML lifecycle - from training to deployment - using AWS SageMaker and modern DevOps practices. This is an engineering-focused role, not a mathematical modeling one.

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What You’ll Do

• Build and maintain ML pipelines using SageMaker for training and deployment.

• Work with data scientists to productionise models and manage deployments.

• Develop tools and workflows for CI/CD, monitoring, and model versioning.

• Ensure infrastructure is scalable, secure, and robust.

• Automate model lifecycle processes to support rapid iteration and reliability.

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What You’ll Need

• Strong experience in ML Ops with a focus on machine learning systems.

• Proficiency with AWS SageMaker, Python, Docker, and workflow orchestration tools.

• Familiarity with infrastructure-as-code (e.g., Terraform, CloudFormation).

• Experience deploying and monitoring models in production environments.

• Understanding of CI/CD and best practices for ML.

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Nice to Have

• Exposure to energy trading or real-time data environments.

• Experience with tools like MLflow, Airflow, or Step Functions.

Apply now for immediate review!