Build meaningful technology with accountable teams

Job description

The Machine Learning Engineer will bridge data science and production engineering by packaging, deploying and operating ML capabilities.

Key responsibilities

• Implement model-training and inference pipelines.
• Build APIs, batch or event-driven model-serving integrations.
• Automate testing, versioning, deployment and monitoring for ML workloads.
• Partner with data scientists on evaluation and reproducibility.
• Improve performance, cost, security and production reliability.

Qualifications

• 4–8 years of software, data or machine-learning engineering experience.
• Strong Python and practical ML framework experience.
• Experience deploying models into production environments.

Preferred qualifications

• MLflow, Databricks, Azure ML or SageMaker experience.
• Docker/Kubernetes and CI/CD experience.
• Feature-store, streaming or real-time inference experience.

Benefits & employment terms

• Compensation, leave, pension and any role-specific benefits are confirmed during the recruitment process and stated in the written offer.
• Any client-site, travel, security-screening or right-to-work requirements are confirmed before appointment.

Nature of working style

• Work is organised around defined delivery outcomes, documented responsibilities, peer review and clear escalation paths.
• Hybrid or client-site attendance varies by engagement and is confirmed before assignment.

Location

London-based with UK client-site collaboration where required.