MLOps Engineering Manager

Department: Technology

Location: London

Employment Type: FullTime

About us

We are champions of rail, inspired to build a greener, more sustainable future of travel. Trainline enables millions of travellers to find and book the best value tickets across carriers, fares, and journey options through our highly rated mobile app, website, and B2B partner channels. 

Great journeys start with Trainline 🚄 

Now Europe’s number 1 downloaded rail app, with over 135 million monthly visits and £6.3 billion in annual ticket sales, we collaborate with 270+ rail and coach companies in over 40 countries. We want to create a world where travel is as simple, seamless, eco-friendly and affordable as it should be. 

Today, we're a FTSE 250 company driven by our incredible team of over 1,000 Trainliners from 50+ nationalities, based across London, Paris, Barcelona, Milan, Edinburgh and Madrid. With our focus on growth in the UK and Europe, now is the perfect time to join us on this high-speed journey. 

Introducing the Trainline Machine Learning and AI Team 👋
Machine Learning and AI play an important role in Trainline’s mission to help millions of people make more sustainable travel choices every day. Our models and AI-powered systems support critical areas of our platform, from customer support agents and search recommendations to pricing, routing optimisation, personalised experiences and digital marketing.
Our Machine Learning and AI teams own the full delivery lifecycle, from early ideas through to production systems that create measurable impact for our customers and the business. As MLOps Engineering Manager, you will help shape how we build, deploy and operate machine learning products at scale, working closely with ML Engineers, Data Engineers, Software Engineers, Data Scientists, Product Managers and stakeholders across Trainline.

In this role as the MLOps Engineering Manager, you will... 🚄

• Build and lead a new team of MLOps Engineers, creating an environment where people can do their best work while delivering meaningful outcomes for customers and the business.

• Define and evolve MLOps processes, tooling and infrastructure choices across the technology department, helping teams build scalable, reliable and maintainable machine learning and AI systems.

• Own the deployment and operation of machine learning products, ensuring models and AI systems are production-ready, observable and able to support Trainline’s growth.

• Partner closely with engineering, data science, product and data teams to bring strong engineering standards into machine learning delivery, while recognising the specific challenges of data, AI and ML systems.

• Support the productionisation of batch and online machine learning models, including recommendation systems, classification and regression models, large language models and agent-based systems.

• Promote high standards for experimentation, testing, monitoring and continuous improvement, helping teams learn quickly and make evidence-led decisions.

• Contribute actively to Trainline’s AI and ML community, sharing knowledge, shaping best practice and supporting a culture of collaboration, curiosity and impact.

• Help the team make thoughtful technology choices across cloud infrastructure, CI/CD, monitoring and MLOps tooling, with a focus on long-term maintainability and measurable business value.

We'd love to hear from you if you have... 🔎

• Experience leading, managing or mentoring engineers, with a thoughtful and inclusive approach to developing people, building teams and supporting delivery.

• Strong experience productionising machine learning models at scale, ideally across both batch and online use cases such as recommendation systems, classification models, regression models, large language models or AI agents.

• A good understanding of the machine learning development lifecycle, including data extraction, feature engineering, modelling, evaluation, deployment and ongoing monitoring.

• Experience with cloud infrastructure, ideally AWS, alongside DevOps technologies and practices such as Docker, Terraform, CI/CD pipelines and infrastructure-as-code.

• Familiarity with MLOps tools and practices such as MLflow, Airflow, model monitoring, API monitoring, data validation, data drift detection, autoscaling and access management.

• Strong Python experience, with helpful knowledge of Spark or PySpark for working with large-scale data and machine learning systems.

• An understanding of feature stores and related data technologies used to support operational machine learning products.

• Clear communication skills, with the ability to work effectively across engineering, data, product and business teams, and explain technical concepts in an accessible way.

More information:

Enjoy fantastic perks like private healthcare & dental insurance, a generous work from abroad policy, 2-for-1 share purchase plans, an EV Scheme to further reduce carbon emissions, extra festive time off, and excellent family-friendly benefits. 

We prioritise career growth with clear career paths, transparent pay bands, personal learning budgets, and regular learning days. Jump on board and supercharge your career from day one! 

We're operating a hybrid model and ask that Trainliners work from the office a minimum of 60% of their time over a 12-week period. We also have a 28-day Work from Abroad policy.

Our values represent the things that matter most to us and what we live and breathe everyday, in everything we do: 

• 💭 Think Big - We're building the future of rail 

• ✔️ Own It - We focus on every customer, partner and journey 

• 🤝  Travel Together - We're one team 

• ♻️ Do Good - We make a positive impact 

We know that having a diverse team makes us better and helps us succeed. And we mean all forms of diversity - gender, ethnicity, sexuality, disability, nationality and diversity of thought. That's why we're committed to creating inclusive places to work, where everyone belongs and differences are valued and celebrated.

Interested in finding out more about what it's like to work at Trainline? Why not check us out on LinkedIn, Instagram and Glassdoor!