ML Engineer / Senior ML Engineer

Salary: Competitive Salary

A well-known organisation in the financial services & insurance space is building out its MLOps capability and needs someone to own the path from trained model to production system.

The Role

• Productionise machine learning models built by data scientists, ensuring robustness, scalability and performance
• Design feature pipelines, model training workflows and inference services
• Build and maintain CI/CD pipelines for ML models, covering training, testing, deployment and monitoring
• Implement model versioning, experiment tracking and reproducibility standards
• Set up monitoring for model performance, drift, data quality and operational health
• Operate ML workloads on cloud platforms, with containerisation and orchestration where it makes sense
• Embed responsible AI practices, including explainability and bias monitoring, in a regulated environment

What You'll Bring
Must-haves:

• Over 3 years in ML engineering, MLOps, or software/data engineering with ML in production
• Strong Python skills (familiarity across scikit-learn, TensorFlow, PyTorch or XGBoost is preferred)
• Experience building and operating ML pipelines in a production environment
• Solid software engineering fundamentals: testing, CI/CD, version control
• Experience with cloud platforms and managed ML services

Nice to haves:

• Background in financial services or insurance
• Familiarity with MLflow, Kubeflow, Airflow, SageMaker, Vertex AI or Azure ML
• Exposure to model explainability techniques such as SHAP or LIME

Why This Role
You'll sit right at the intersection of data science, engineering and cloud, with a direct hand in how AI gets deployed and trusted across the business. It's a role for someone pragmatic and delivery-focused who wants ownership of the platform, not just the pipeline.

How to Apply
Apply now, or reach out to Bruce Batters at Momentum Consulting for a confidential chat: [email protected]