MLOps Engineering Manager

Overview

As MLOps Engineering Manager, you will build and lead a new team of MLOps Engineers to deliver scalable ML products. You’ll define tooling, processes and infrastructure for reliable AI systems that support Trainline’s growth. You’ll own deployment and operation of ML products to ensure production readiness and observability. You’ll collaborate closely with engineering, data science and product teams to raise engineering standards in ML delivery, balancing data, AI challenges with maintainable systems. This role offers impact across customer experiences, pricing, routing and personalization within a mission-driven, sustainability-focused rail platform.

Pay / Benefits

• private healthcare & dental insurance
• work from abroad policy
• 2-for-1 share purchase plans
• EV Scheme to reduce carbon
• extra festive time off
• family-friendly benefits

• Build and lead a new MLOps engineering team, creating an environment for high performance and meaningful outcomes.
• Define and evolve MLOps processes, tooling and infrastructure to support scalable, reliable ML systems.
• Own deployment and operation of ML products ensuring production readiness and observability.
• Partner with engineering, data science, product and data teams to embed strong engineering standards in ML delivery.
• Support productionisation of batch and online models (recommendations, classification, regression, LLMs, AI agents).
• Promote experimentation, testing, monitoring and continuous improvement to enable evidence-led decisions.
• Contribute to Trainline’s AI/ML community, sharing knowledge and best practices.
• Make thoughtful technology choices across cloud infra, CI/CD, monitoring and MLOps tooling for long-term maintainability and business value.

• Experience leading or mentoring engineers with inclusive leadership.
• Proven track record of productionising ML models at scale (batch and online).
• Understanding of ML lifecycle: data extraction, feature engineering, modelling, deployment, monitoring.
• Experience with cloud infrastructure (ideally AWS) and DevOps tooling (Docker, Terraform, CI/CD, IaC).
• Familiarity with MLOps tools like MLflow, Airflow, model monitoring, API monitoring, data validation, drift detection, autoscaling, access management.
• Strong Python, with knowledge of Spark/PySpark for large-scale data and ML systems.
• Understanding of feature stores and data technologies for operational ML products.
• Clear communication across engineering, data, product and business teams.

• Collaborative and inclusive leadership
• Strong communication across technical and non-technical stakeholders
• Bias toward experimentation and data-driven decision making
• MLOps
• AWS
• Docker