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Job Description

Lead Machine Learning Engineer – Retail

x2 days per week in a central London office (hybrid)

About the Company

We’re working with a fast-scaling AI organisation that partners with large product and platform-led businesses to deliver machine learning systems that support personalisation, demand forecasting, and operational resilience. Their work helps clients enhance customer experience, optimise fulfilment and logistics, and make smarter, data-driven decisions.

This is a senior-level technical role with plenty of scope to shape the architecture, tooling, and delivery practices across impactful applied AI projects.

What You’ll Be Doing

Lead the design and development of robust machine learning platforms that power core business functions across multiple client environments Set technical strategy across projects and drive execution on model development, deployment workflows, and infrastructure Collaborate with engineers, product teams, and stakeholders to translate high-level objectives into scalable ML solutions Build shared tools and frameworks that support consistency and reusability across the engineering function Guide project scoping and delivery plans while helping define best practices around ML system design Mentor engineers at varying levels and contribute to hiring, capability building, and tooling decisions Help clients understand trade-offs and solution architecture, acting as the senior point of technical contact throughout delivery

What They’re Looking For

Strong experience leading end-to-end ML engineering projects, ideally with exposure to customer-centric, high-traffic environments Proficiency in Python and hands-on experience with ML frameworks such as PyTorch, TensorFlow, or Scikit-learn Familiarity with cloud-based deployment workflows and infrastructure management (e.g. AWS, Azure, or GCP) Real-world experience working with Docker, Kubernetes, and production ML pipelines Strong communication skills and comfort engaging across technical, commercial, and executive teams A pragmatic and detail-oriented approach to balancing experimentation with reliable, scalable delivery

If this role interests you and you would like to find out more (or find out about other roles), please apply here or contact us via niall.wharton@Xcede.com (feel free to include a CV for review).

If you are interested in applying for this job please press the Apply Button and follow the application process. Energy Jobline wishes you the very best of luck in your next career move.