Machine Learning Engineer

Machine Learning Engineer – Fraud Detection Contract: 6 months Engagement: Inside IR35 Location: Fully remote, with occasional travel to the London office. The Role We're looking for an experienced Machine Learning Engineer to help scale and optimise our production fraud detection platform. Working alongside Data Scientists and Software Engineers, you'll build, deploy and continuously improve machine learning solutions that detect fraud in real time. You'll be responsible for taking analytical improvements into production, ensuring models remain scalable, performant and measurable. You'll play a key role in evolving the engineering capabilities behind our fraud detection platform while contributing to improvements in model accuracy and operational performance. Responsibilities Build and maintain production machine learning pipelines. Deploy and optimise fraud detection models. Develop scalable Python solutions for model execution and data processing. Work with Azure Data Lake and large-scale datasets. Partner with Data Scientists to productionise new model features. Monitor model performance and identify optimisation opportunities. Improve model efficiency and operational reliability. Optimise classification performance against F1 Score, Precision and Recall. Build tooling to support experimentation, evaluation and deployment. Collaborate with Product and Engineering teams to deliver production-ready machine learning capabilities. Essential Skills Commercial experience as a Machine Learning Engineer or Software Engineer with strong ML experience. Strong Python. Strong Pandas and SQL. Experience working with Azure Data Lake or equivalent cloud platforms. Experience deploying and maintaining production machine learning models. Experience working with large-scale datasets. Strong software engineering practices. Experience building production data pipelines. Understanding of classification models and model evaluation. Experience improving production model performance. Desirable Fraud detection. Financial crime. Behavioural biometrics. Device intelligence. Real-time decisioning systems. Payments or banking. ML deployment frameworks. CI/CD for machine learning. MLOps.