Machine Learning Engineer
10 hours ago
London
Machine Learning Engineer - Contract (Financial Services, Outside IR35) Duration: 6 months Rate: £650 - £750 per day IR35: Outside Location: UK / Remote We’re seeking an experienced Machine Learning Engineer to support a Financial Services organisation on an initial 6-month contract, working on production-grade ML systems that operate in regulated, high-volume environments. This role is ideal for someone comfortable taking models from research through to deployment, with a strong appreciation for robust engineering, governance, and scalability. Responsibilities • 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 Required Experience • 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 Contract Details • £650–£750 per day (Outside IR35), • Initial 6-month contract, with strong extension potential, • Immediate or short-notice start preferred