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# Lead Machine Learning Engineer

- **Company:** Xcede
- **Location:** Greater London, UK
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Python (Programming Language), Machine Learning, Tensorflow, Azure Machine Learning, Workflow Management Systems, Pytorch, Delivery Pipeline, Scikit Learn, Kubernetes, Machine Learning Operations, Docker
- **Published:** September 10, 2026
- **Apply:** https://www.collegerecruiter.com/job/2833332956-lead-machine-learning-engineer

## About the Role

* Deep technical experience delivering ML applications from prototype through to live production, ideally across commercial or B2B environments
 * Strong Python development skills and experience with ML frameworks like PyTorch, TensorFlow, or Scikit-learn
 * Familiarity with deployment workflows in cloud platforms such as Azure, GCP, or AWS
 * Experience managing containerised infrastructure using Docker and orchestration tools like Kubernetes
 * Strong communication skills and ability to lead teams through both technical complexity and cross-functional planning
 * A background in building reusable components, standardising engineering practices, and improving developer experience across teams
 * Comfort working directly with client teams, supporting technical delivery while managing competing business priorities

## Description

* Lead technical delivery across multiple applied machine learning projects, including model design, architecture, and deployment
 * Act as the key decision-maker on system design, scaling strategies, and tool selection for critical ML services
 * Shape project roadmaps and guide engineers working on complex problems involving uncertainty, ambiguity, and evolving client needs
 * Develop shared tools and internal infrastructure to accelerate delivery and maintain quality across the wider engineering function
 * Mentor and manage engineers, help define hiring standards, and contribute to capability building across technical teams
 * Represent engineering in cross-functional forums, working closely with product, strategy, and client stakeholders
 * Support clients with architectural planning, deployment decisions, and technical coaching during delivery
 * Drive adoption of new tools, frameworks, or practices that strengthen internal ML workflows and delivery velocity


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- [Getting Started with Machine Learning](https://www.wearedevelopers.com/videos/260-getting-started-with-machine-learning)

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