Lead Machine Learning Engineer

Xcede; Greater London; Hybrid; GBP 120,000 - 150,000; Full time

Job summary

Xcede in London is seeking a Lead/Principal Machine Learning Engineer to drive technical delivery across multiple ML projects. The role combines model design, deployment, and architectural decisions with leadership across engineering teams in a hybrid central London setting.

You will guide roadmaps, mentor engineers, and collaborate with product and client stakeholders to deliver high-impact ML solutions that solve real business problems.

Qualifications

• Deep technical experience delivering ML applications from prototype through to live production.
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Responsibilities

• Lead technical delivery across multiple applied ML 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 on complex problems with uncertainty and evolving client needs
• Develop shared tools and internal infrastructure to accelerate delivery and maintain quality
• Mentor and manage engineers, contribute to hiring standards and capability building
• Represent engineering in cross-functional forums with product, strategy, and client stakeholders
• Support clients with architectural planning, deployment decisions, and technical coaching
• Drive adoption of new tools, frameworks, or practices to strengthen ML workflows

Skills

Python development

ML frameworks (PyTorch, TensorFlow, Sc

Cloud deployment (Azure, AWS, GCP)

Docker & Kubernetes

Leadership & mentoring

Communication

Reusable components/engineering

Tools

PyTorch

TensorFlow

Scikit-learn

Azure

AWS

GCP

Docker

Kubernetes

Job description

Lead / Principal Machine Learning Engineer

x3 days per week in a central London office (hybrid model)

About the Company

We’re working with an AI-first organisation that supports enterprise-scale clients in solving complex challenges across sectors such as finance, insurance, and customer intelligence. The team partners with large corporates to build machine learning solutions that bring commercial value, automate decisions, and enhance resilience across business operations.

You’ll be joining a high-performance technical group focused on helping clients adapt to shifting markets and transform legacy systems using AI. Projects often involve exploratory problem-solving, long-term system design, and building reusable solutions that deliver real business outcomes.

What You’ll Be Doing

• 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

What They’re Looking For

• 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