Lead Machine Learning Engineer – Consumer & National Energy Systems
x2–3 days a week in a central London office (hybrid)
About the Company
This AI advisory group partners with large-scale operators across infrastructure, energy, and environmental systems to design and deploy applied machine learning solutions. Their mission is to unlock measurable value across national networks, complex supply systems, and fast-evolving green technologies.
You’ll join a technically elite team working on high-impact use cases in the consumer energy space, helping major organisations optimise decision-making, automate processes, and unlock predictive insights at scale.
What You’ll Be Doing
- Lead the technical design and build of complex machine learning platforms that operate across distributed, high-volume energy systems
- Scope out engineering roadmaps for challenging projects and drive delivery across multiple technical workstreams
- Architect and scale ML-driven platforms, ensuring clean integration, maintainability, and performance in live environments
- Build shared tooling and infrastructure that supports long-term platform growth across teams and clients
- Provide senior input on system design decisions, balancing exploratory freedom with robust technical frameworks
- Take ownership of infrastructure decisions, ensuring consistency across development, deployment, and observability layers
- Act as a trusted technical advisor, guiding clients and internal teams through applied ML challenges from concept to production
- Play a key role in growing internal engineering capability, contributing to hiring, mentoring, and technical development
What They’re Looking For
- Strong engineering leadership experience in ML-focused environments, with a focus on productionisation and long-term scalability
- Proficient in Python, with hands-on experience using frameworks like PyTorch, TensorFlow, or scikit-learn in applied settings
- Proven background in deploying ML systems on cloud platforms such as AWS, Azure, or GCP
- Familiar with containerisation and orchestration tools such as Docker and Kubernetes
- Experienced in working across ambiguous or high-risk environments, with the ability to structure delivery and define scope
- Strong communication skills, with confidence engaging senior stakeholders and translating between technical and strategic goals
- Bonus: exposure to systems built for large-scale energy or infrastructure use cases, especially those involving real-time optimisation or data-rich forecasting
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).
