Senior Machine Learning Engineer

Why Apply? This is an opportunity to shape how enterprise machine learning is delivered at scale within a modern data environment. Working on a strategic Lakehouse platform built on Databricks, you will influence how production ML models are designed, governed and optimised, helping the business turn complex data into reliable, decision-driving insight. The role combines hands-on engineering with MLOps leadership, strong stakeholder engagement and long-term platform thinking.

• Lead the design, build, deployment and monitoring of production machine learning models using Databricks, ensuring performance, reliability and continuous improvement
• Define and embed MLOps best practices including model versioning, governance, access control, monitoring and retraining strategies
• Develop automated model validation tests covering unit, integration, regression and bias checks
• Translate business problems into effective ML solutions, managing ethical, privacy and data governance considerations
• Establish model performance KPIs, reliability measures and production monitoring frameworks
• Document model design, assumptions, metrics, risks and failure scenarios, ensuring full data and model traceability
• Diagnose and resolve production ML issues, leading root cause analysis and system improvements
• Work within cross-functional agile teams and support knowledge sharing across the wider business

• Degree in a STEM subject or equivalent experience with strong statistical understanding
• Proven experience delivering machine learning models into production environments
• Strong Python, SQL and PySpark skills for scalable, production-grade development
• Hands-on experience establishing MLOps processes within Databricks
• Experience building data pipelines for structured and unstructured data
• Strong stakeholder communication skills, able to explain technical concepts to non-technical audiences
• Experience working in agile delivery environments and managing shifting priorities

• Hybrid working model
• Discretionary bonus
• Non-contributory pension
• Private medical and dental cover (including family options)
• Life insurance and wellbeing-focused benefits
• Supportive, collaborative data and technology environment
• Opportunity to work on modern ML, MLOps and Lakehouse technologies at scale