successfactors
Senior Machine Learning Operations (MLOPS) Engineer
Royal London
GBR
Full-time
Senior
Hybrid
Discovered 2 weeks ago
DatabricksMLflowAzure MLAzureAzure DevOpsGitHub Actions
Free
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DatabricksMLflowAzure ML
Role Overview
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.
Key Skills for This Role
DatabricksMLflowAzure MLAzureAzure DevOpsGitHub Actions
Full Job Posting
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Contract Type: Permanent Location: Alderley Park (Wilmslow) Working Style: Hybrid - 50% from home / 50% office based
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.
Working closely with data scientists, data engineers and platform teams, you’ll 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.
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