Machine Learning Engineer

• Design, build, and deploy machine learning models into production within a Financial Services environment
• Collaborate closely with Data Scientists, Software Engineers, Risk, and Product teams
• Build and maintain end-to-end ML pipelines (training, validation, inference, monitoring)
• Ensure models meet requirements around performance, resilience, and explainability
• Contribute to MLOps best practices, model governance, and technical standards
• Support model monitoring, drift detection, and ongoing optimisation

• Proven commercial experience as a Machine Learning Engineer, ideally within Financial Services, FinTech, or a regulated environment
• Strong Python skills and hands-on experience with ML libraries (TensorFlow, PyTorch, scikit-learn)
• Experience deploying and supporting ML models in production
• Solid understanding of data pipelines, versioning, testing, and software engineering best practices
• Experience working with cloud platforms (AWS, GCP, or Azure)

Nice to Have

• Experience with fraud, risk, credit, AML, pricing, or customer analytics use cases
• Familiarity with MLOps tools (MLflow, Kubeflow, Airflow, etc.)
• Docker and Kubernetes experience
• Exposure to model governance, explainability, or regulatory frameworks