Senior Staff MLOps Engineer

Location: London | Hybrid (3/4 days in office)

We are looking for a Senior Staff MLOps Engineer to join a growing AI function within a global, organisation focused on developing medicines for patients.

The team is developing production-quality AI capabilities using complex biomedical data, with the aim of turning cutting-edge machine learning research into tools and models that scientists can reliably use, trust and scale.

This is a senior technical leadership position, rather than a traditional line-management role. You will lead on MLOps technology, architecture and best practice, mentor engineers and help establish how the function operates as it grows. The role is about taking innovative ML work and turning it into something robust, reliable and operational.

You will look across the entire model lifecycle, from training and experimentation through to deployment, monitoring, and maintenance.

Responsibilities include:

• Defining the MLOps architecture, strategy and technical roadmap.
• Establishing standards and best practices for taking models from research into production.
• Building highly reliable ML services that scientists can trust and use consistently.
• Supporting models once they are live, identifying issues and ensuring problems can be resolved quickly.
• Establishing approaches to experiment tracking, model registries, versioning, lineage and model governance.
• Supporting large-scale and distributed model training across multiple machines.
• Developing approaches to CI/CD, model deployment, serving and monitoring.
• Partnering with Data Engineering teams to ensure data is prepared appropriately for production ML workloads.
• Mentoring engineers and acting as the technical escalation point for the most challenging MLOps problems.

Required skills and experience:

• MSc or PhD in a STEM-related discipline, or equivalent experience.
• Significant (10 years+) relevant experience, with at least a couple of years experience operating at a senior technical level.
• Deep understanding of the MLOps lifecycle and what it takes to run ML systems reliably in production.
• Experience defining technical strategy, architecture and roadmaps.
• Strong knowledge of model registries/repositories, experiment tracking and model versioning.
• Experience with tools such as MLflow or equivalent
• Strong understanding of Git and software/model versioning practices.
• Distributed training experience, including training large models across multiple machines.
• Strong Python and software engineering skills, alongside technologies such as Docker, Kubernetes and Helm.
• Experience supporting systems in a live operational environment, including monitoring reliability, and responding when things go wrong.
• Strong communication and influencing skills – you should be comfortable working with teams outside your immediate function and able to influence technical decisions.

Experience within biomedical, healthcare or life sciences data would be highly advantageous but is not strictly essential. Candidates coming from other complex or highly regulated environments, such as financial services, could also be relevant.

This is a senior position, with lots of opportunity for strategic input and technical influence, whilst still tackling the hardest challenges in MLOps hands on.

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