MLOps Engineering Manager, Trainline

• Have experience productionising batch and online Machine Learning models (such as classification & regression problems, recommendation systems, Large Language Models...) at scale
• Are familiar with the ML development lifecycle and have a broad understanding of data extraction, feature engineering, modelling and evaluation techniques
• Have experience with Cloud infrastructure (ideally AWS), DevOps technologies such as Docker or Terraform and CI/CD processes and tools
• Have previously worked with MLOps tools like MLFlow and Airflow, or on common problems such as model and API monitoring, data drift and validation, autoscaling, access permissions..
• Have previously worked with monitoring tools such as New Relic or Grafana
• Understand the use of feature stores and related data technologies for operational machine learning products
• Are proficient with Python and have Spark knowledge
• Have leadership experience either through previous management or mentorship
• Have good communication skills

Desirable

• We are looking for a MLOps Engineering Manager to join our team and help shape the future of train travel
• You will be part of a highly innovative AI and ML team working alongside engineers, scientists and product managers to tackle complex challenges by combining Trainline’s rich datasets with cutting edge algorithms to build our next generation platform
• What unites our team is an expertise in the field, a love of what we do and the desire to create impactful solutions to support Trainline’s goals of encouraging sustainable travel
• As a part of Trainline you will be joining an environment where learning and development is top priority
• You will have the opportunity to work with fellow ML enthusiasts on large-scale production systems, delivering highly impactful products that make a difference to our millions of users
• Build a new team of MLOps Engineers working alongside ML Engineers, Data Engineers, Software Engineers, Data Scientists and Product Managers
• Define MLOps processes and steer tooling and infrastructure choices across the technology department
• Own the deployment and operations of machine learning products
• Ensure delivery of high-quality, scalable and maintainable machine learning models and AI Systems that drive measurable impact for our business
• Act as a bridge between engineering and data, ensuring engineering standards are met while understanding the specificities of data, AI and machine learning challenges
• Take an active part in our AI and ML community and foster a culture of rigorous experimentation, testing and learning