Senior Machine Learning Engineer

Our client is seeking an experienced Senior MLOps/Machine Learning Platform Engineer to help accelerate the delivery of their next-generation fraud detection platform. This role is fully remote and can be based in the UK or Europe. The Role You'll join a specialist ML Platform team responsible for the infrastructure that underpins fraud detection models. While Data Scientists own the models themselves, this team owns the platform, tooling, reliability, scalability, and deployment frameworks that enable those models to operate in production at scale. The organisation has a significant roadmap and is looking for a seasoned consultant who can quickly contribute, take ownership of key initiatives, and help drive delivery. Key Responsibilities • Enhance and scale a real-time ML platform supporting fraud detection in high-volume payment environments. • Develop and improve feature store capabilities, ensuring online/offline feature parity and self-service data workflows. • Build, optimise, and maintain streaming data pipelines using Kafka and Spark Structured Streaming. • Design and implement robust model deployment processes, including CI/CD, canary releases, shadow testing, and observability. • Improve training pipeline performance, reliability, and cost efficiency across Databricks and AWS environments. • Strengthen platform security, infrastructure resilience, and operational standards through Terraform-led cloud engineering. • Deliver end-to-end AWS platform improvements, including IAM, KMS, dependency management, and infrastructure hardening. • Support multi-region platform operations and contribute to continuous improvements in scalability, reliability, and performance. • Collaborate with data scientists and engineering teams to accelerate delivery of the fraud detection roadmap. Skills & Experience • 5+ years' experience in Data Science, Machine Learning, or MLOps Engineering. • 3+ years' consulting experience. • Strong Python and MLOps/platform engineering background. • Deep AWS knowledge (ECS, EC2, DynamoDB, IAM, KMS) and Terraform. • Hands-on experience with Databricks, PySpark, Delta Lake, Unity Catalog, and MLflow. • Experience with Kafka and Spark Structured Streaming. • Proven track record deploying and operating ML models in production. • Comfortable working independently in low-latency, high-availability environments. If this position sounds of interest, apply with your latest CV. Please note, only shortlisted candidates will be contacted about the role.