MLOps Engineer

Salary: £59,000 - 99,000 per year

• Strong commercial experience in Python and applied ML engineering.
• Proficiency with MLOps tooling such as MLflow and modern experiment tracking platforms.
• Experience deploying models into production, including monitoring, testing, and automation.
• Practical experience with AWS, specifically using SageMaker.
• Solid foundations in Cloud and DevOps, including Docker and AWS.
• Ability to build scalable data and ML pipelines with solid engineering practices.
• Strong communication skills and aptitude for collaboration in fast-paced environments.

• Support data scientists and AI engineers in building, deploying, and monitoring ML models in production environments.
• Manage the ML lifecycle effectively.
• Design scalable ML pipelines for training, validation, and deployment.
• Implement CI/CD workflows for machine learning and maintain reliable ML endpoints.
• Work extensively with AWS, including SageMaker, to deliver robust, secure, and scalable ML infrastructure.
• Apply strong engineering standards across cloud, DevOps, and automation practices.
• Contribute to computer vision and broader ML workloads, with scope to support new AI initiatives as they grow.

• AI
• AWS
• CI/CD
• Cloud
• Computer Vision
• DevOps
• Docker
• Support
• Machine Learning
• Python

More:

We are a fast-growing technology scale-up within the energy and electric space, offering a collaborative and customer-focused team driven by a strong product mindset. This exciting opportunity allows you to join a small, high-impact data group with experienced engineers, where you will have the chance to take real ownership. We provide a competitive salary plus discretionary bonus, hybrid working three days a week in our London office, and a mission-led environment focused on accelerating the transition to clean energy.

last updated 38 week of 2026