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Lead ML Engineer

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Onsite

Lead ML Engineer

Harnham

Lead ML Engineer

Skills

Google Cloud PlatformDockerPythonData ScienceMachine LearningAWSKubernetesApache AirflowAzure

3 days ago

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Important information


Contract type:

Permanent

Salary:

£110,000 – £120,000 / year

Location:

London, UK

Work mode:

Onsite

Published on:

24 October 2026

What they need


Lead Machine Learning Engineer

London (Hybrid) | £110,000 - £120,000

This is an excellent opportunity for a hands-on Machine Learning Engineer who is keen to take the next step into leadership. You'll continue building and deploying production-grade ML systems while gradually taking on team management responsibilities, offering the chance to shape both technical strategy and team growth.

The Company

A high-growth technology business with a strong focus on machine learning and data-driven decision-making. They are investing heavily in their ML capabilities and are looking for a technical leader to help scale their machine learning infrastructure, engineering best practices, and team capability. You'll join a collaborative environment where engineering excellence and innovation are highly valued.

The Role

As a Lead Machine Learning Engineer, you will remain deeply involved in the technical delivery of machine learning solutions while mentoring a growing team.

Responsibilities include:

  • Build and maintain production-grade machine learning systems and pipelines.
  • Remain hands-on with coding, code reviews, debugging, and deployments.
  • Mentor and support a small team of Machine Learning Engineers.
  • Establish machine learning engineering and MLOps best practices.
  • Improve deployment, monitoring, and model lifecycle management processes.
  • Collaborate closely with Data Science, Risk, Product, and Engineering teams.
  • Contribute to technical roadmap planning and future hiring initiatives.

Your Skills & Experience

  • Strong background as a Machine Learning Engineer.
  • Experience deploying and maintaining machine learning models in production environments.
  • Strong software engineering capabilities, including Python, APIs, and distributed systems.
  • Good understanding of MLOps tooling, including CI/CD, monitoring, and pipeline management.
  • Experience with tools such as MLflow, Airflow, Evidently, DVC, Docker, or Kubernetes.
  • Experience working within AWS, GCP, or Azure environments.
  • Previous mentoring or leadership experience with an interest in developing management responsibilities.

How to Apply

If you're interested in combining hands-on machine learning engineering with a clear pathway into leadership, please apply with your CV for immediate consideration.

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