Senior Machine Learning Engineer

Xcede; Greater London; On-site; GBP 90,000 - 130,000; Full time

Job summary

Xcede is seeking a Senior Machine Learning Engineer to join cross-functional delivery teams on technically complex AI projects. You’ll lead the design and build of ML architecture—from deployment to tooling—to ensure scalable, secure solutions in production environments.

This role involves hands-on work with modern MLOps practices and collaboration across disciplines. This hands-on role offers scope for team leadership and close collaboration with customers and stakeholders to deliver

Qualifications

• Strong Python software engineering skills.
• Experience deploying ML models to production.
• Experience with cloud infrastructure (AWS/Azure/GCP).
• Hands-on experience with Docker and Kubernetes in real-world workflows.
• Excellent communication in client-facing settings.

Responsibilities

• Lead the design and build of production-ready ML 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 to performance optimization.
• Collaborate with customers and stakeholders to understand constraints and align on design.
• Mentor junior engineers and shape internal technical standards.
• Support continuous improvement of delivery practices and tooling.

Skills

Python

ML deployment

Cloud experience

Docker

Kubernetes

Communication

Tools

Docker

Kubernetes

AWS/Azure/GCP

Job description

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.

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