ML Ops Engineer | York Hybrid

Machine Learning Engineering Manager We're building a new ML Engineering team and are looking for a strong technical lead to help take our machine learning capability from proof-of-concept to fully scaled, production-ready solutions. Sitting within our Group & Enterprise Services (GES) function, this role is part of the Data vertical and reports into the Head of Data Engineering. You'll be hands-on with cloud infrastructure, APIs and deployment pipelines, working mainly in GCP Vertex AI (essential) and Azure (desirable). Your focus will be enabling data scientists to deploy high-impact models reliably and at scale. You'll combine leadership, architectural thinking and deep engineering skills to shape the ML platform, coach engineers and deliver robust, enterprise-ready ML services. What you'll do * Lead, mentor and develop a small team of ML Engineers * Oversee delivery of ML capabilities and support planning and capacity needs * Shape architecture from early design through to production * Build and maintain Python APIs (Flask/FastAPI) for model serving * Develop infrastructure for real-time and batch deployments * Design and maintain CI/CD pipelines for models * Ensure code quality, engineering best practice and scalable cloud deployments * Collaborate with data scientists, platform engineers and developers * Support model lifecycle management, monitoring and automation * Break down solution designs into deliverables and milestones What you'll bring * 5+ years as an ML Engineer with strong Python engineering skills * Experience deploying and maintaining ML models in production (Vertex AI required) * Strong software engineering fundamentals: OOP, unit testing, TDD * Cloud experience (GCP, AWS or Azure) and IaC tools such as Terraform * Experience with Docker, CI/CD pipelines and Git workflows * Understanding of data science principles and taking research code to production * Strong problem-solving skills and the ability to work independently * Comfortable working in Agile teams * Clear communication, collaboration and a proactive, improvement-driven mindset