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Senior Machine Learning Engineer
x2 days a week on UK client site, optional London HQ visits
About the Company
We’re partnering with a specialist AI and data consultancy that designs and deploys bespoke machine learning systems across sectors such as national security, defence, critical infrastructure, and digital public services. Their focus is on delivering safe, production-grade AI solutions that drive real-world outcomes for complex, high-stakes environments.
This is a fast-paced, technically elite environment, ideal for someone who thrives on solving operational challenges, building robust MLOps infrastructure, and leading the delivery of AI systems at scale.
The Role
As a Senior Machine Learning Engineer, you’ll be part of cross-functional delivery teams working on technically complex, high-impact AI projects. You’ll play a central role in designing and building the ML architecture (from infrastructure and deployment to tooling and automation) to ensure that solutions are not only technically sound, but also scalable, maintainable, and secure.
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This is a hands-on role with scope for team leadership, stakeholder engagement, and shaping best practices around modern MLOps. You’ll work alongside data scientists, engineers, designers, and product stakeholders often embedded within mission-critical delivery environments.
Key Responsibilities
- Lead the design and build of production-ready machine learning pipelines and systems
- Develop infrastructure and tooling to enable deployment, monitoring, and retraining of ML models
- Work across the full AI delivery lifecycle, from architecture and integration to performance optimisation
- Collaborate with customers and stakeholders to understand operational constraints and align on solution design
- Mentor junior engineers and shape internal technical standards for software quality, reliability, and reproducibility
- Support the continuous improvement of delivery practices, internal tooling, and knowledge sharing across teams
What We’re Looking For
- Strong software engineering skills, especially in Python.
- Experience building robust systems for ML applications
- Proven track record deploying machine learning models in production (using frameworks such as Scikit-learn, TensorFlow, or PyTorch)
- Practical experience working with cloud infrastructure (e.g., AWS, Azure, GCP) and a good understanding of architecture, security, and scaling
- Hands-on experience with Docker and Kubernetes in real-world engineering workflows
- Solid grasp of ML fundamentals: supervised/unsupervised learning, statistical modelling, evaluation
- A pragmatic approach to engineering capable of balancing speed, risk, and delivery in complex environments
- Excellent communication and collaboration skills, especially in client-facing settings
- Prior experience in a fast-paced or start-up environment is highly valued
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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).
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