Apply for Senior Machine Learning Operations (MLOPS) Engineer at Royal London Group in Alderley Edge, ENG, GB. This full-time on‑site position offers great opportunities for career growth.
Job Overview
We have an exciting opportunity for a Senior Machine Learning Operations Engineer to join Royal London’s Group Data and AI Office. In this role, you’ll provide senior technical leadership for the workflows, tooling and engineering practices that take machine learning safely and reliably from experimentation into production. You’ll work closely with data scientists, data engineers and platform teams to define and evolve standards for CI/CD, experiment tracking, model lineage and controlled promotion across environments.
Using Databricks, MLflow and Azure ML, you’ll help create scalable, well‑governed pipelines that are reproducible, traceable and auditable. You’ll also embed model risk management into delivery, supporting repeatable builds, clear lineage, transparent decision points and audit trails that strengthen governance and reduce operational risk. As a senior practitioner, you’ll champion engineering excellence, reusable patterns and production‑ready ways of working. You’ll mentor colleagues, support communities of practice and influence platform and architecture decisions so ML products deliver sustainable business value at scale.
Responsibilities
- Provide senior technical leadership for machine learning workflows, tooling and engineering practices from experimentation to production.
- Collaborate with data scientists, data engineers and platform teams to define and evolve standards for CI/CD, experiment tracking, model lineage and controlled promotion across environments.
- Design and evolve scalable, production‑grade ML workflows on Databricks.
- Lead effective use of MLflow for experiment tracking, model versioning, lineage and lifecycle management.
- Build reusable CI/CD patterns for model training, validation, promotion and inference.
- Champion strong engineering practices, including modular Python, testing, reproducibility and configuration management.
- Work with data scientists and platform teams to productionise models safely and efficiently.
- Mentor colleagues and support high standards across the team.
- Embed model risk management into delivery, supporting repeatable builds, clear lineage, transparent decision points and audit trails.
- Champion engineering excellence and production‑ready ways of working.
- Influence platform and architecture decisions to ensure ML products deliver sustainable business value at scale.
Qualifications
- Strong experience with Azure, including Azure ML, Data Factory and Azure DevOps.
- Hands‑on experience with Databricks, MLflow and production‑grade ML workflows.
- Experience designing CI/CD pipelines using Azure DevOps, GitHub Actions or similar tools.
- Advanced Python skills, with knowledge of SQL, Spark and testing approaches for ML.
- Understanding of data engineering concepts such as ETL/ELT, feature stores and lineage tracking.
- Awareness of security, governance, Responsible AI and relevant AI regulation.
Benefits
We offer great benefits, including 28 days’ annual leave plus bank holidays, up to 14% employer pension matching and private medical insurance.
Equal Opportunity Statement
We’re an inclusive employer and welcome applications from people of all backgrounds. We value the different perspectives, experiences and skills our colleagues bring.
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