Machine Learning Engineer at Three UK

As ML Engineer you will be responsible for bridging the gap between data science experimentation and production-grade, scalable ML systems. You will own the engineering excellence required to take validated models from data scientists and deploy them reliably across the organisation's fragmented platform estate. You will work across the full ML Operations lifecycle—designing deployment pipelines, implementing model serving infrastructure, establishing monitoring and governance frameworks, and automating retraining workflows. Your role is critical to standardising practices across platforms and ensuring models can be built, deployed, and maintained consistently regardless of underlying infrastructure differences. You will collaborate closely with Data Scientists on model productionisation, AI Engineers on platform infrastructure requirements, and Analytics Engineering on data pipeline dependencies and reliability. Key Roles & Responsibilities • Design, build, and maintain ML deployment pipelines and model serving infrastructure for both real-time and batch inference workloads across multiple platforms • Establish comprehensive model monitoring, alerting, and performance tracking systems in production environments to ensure reliability and early problem detection • Implement model versioning, reproducibility, and automated retraining workflows that enable fast iteration whilst maintaining stability • Partner with Data Scientists to productionise validated experimental models, translating research outputs into robust, maintainable systems • Contribute to platform standardisation efforts across the fragmented estate, identifying common patterns and opportunities for reuse • Design and implement CI/CD pipelines tailored for ML workloads, ensuring quality, traceability, and repeatability • Support governance and compliance requirements through technical documentation, audit trails, and reproducible deployment processes • Monitor and optimise compute resource allocation and infrastructure costs across platforms, applying FinOps principles • Collaborate with Analytics Engineering to ensure data pipeline reliability, quality, and performance for ML workloads • Contribute to team knowledge sharing and best practice documentation across the ML Engineering function