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Senior MLOps Engineer

London · London, Greater London, United Kingdom

Pay
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Work setting
Hybrid — work setting source
Location type: Hybrid
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Employment
Full-time — employment source
Employment type: Full-time
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Team
Technology › Data — team source
Department: Technology › Data
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Before you apply

Sponsorship
Visa sponsorship not confirmed — sponsorship source
Do you require sponsorship now or in the future to work in the job's location? We may provide sponsorship support to candidates on a role by role basis.
From the employer’s application form
Apply at Trainline

What you’ll work on

Full posting
  • Build one standard way to deploy ML and AI products

  • Support AI infrastructure with governance, monitoring and automated deployment

  • Automate service modernisation and fix security vulnerabilities in dependencies

  • Improve cost tracking and reduce MLOps costs

  • Contribute to technical strategy and make decisions without established approaches

From the employer’s posting
Build and drive adoption of one standard way to deploy ML and AI products
Support AI infrastructure, working with the teams building our internal platform and customer-facing products, making sure products are well governed, monitored and deployed through automation
Automate the modernisation of our services across products, including fixing and preventing security vulnerabilities in libraries and dependencies
Strengthen our MLOps foundations by improving cost tracking, finding ways to reduce costs
Contribute to technical strategy, evaluating options and making well-reasoned decisions where there is no established approach

Tools in this posting

  • Python
  • AWS
  • Azure
  • Docker
  • GCP
  • Grafana
  • MLflow
  • SageMaker
  • Terraform
  • Airflow
Source — Tool mentions in context
- Experience with model experiment tracking, ideally MLflow or Weights & Biases, and with monitoring tools such as EvidentlyAI, CometML, Grafana or New Relic - Experience writing production-quality, tested Python code - Experience leading or contributing significantly to major platform changes and migrations, and the ability to lead technical work through ambiguity
We’d love to hear from you if you have...🔍 - Hands-on experience with IaC and CI/CD tooling, ideally Terraform and GitHub Actions, though alternatives such as AWS CloudFormation and Jenkins are also relevant - Experience with containerised workloads using Docker and cloud platforms, ideally AWS, though GCP and Azure will be considered
- Hands-on experience with IaC and CI/CD tooling, ideally Terraform and GitHub Actions, though alternatives such as AWS CloudFormation and Jenkins are also relevant - Experience with containerised workloads using Docker and cloud platforms, ideally AWS, though GCP and Azure will be considered - Experience productionising AI products, including agentic systems and the AIOps tooling behind them, ideally using AWS Bedrock AgentCore or similar
- Experience with containerised workloads using Docker and cloud platforms, ideally AWS, though GCP and Azure will be considered - Experience productionising AI products, including agentic systems and the AIOps tooling behind them, ideally using AWS Bedrock AgentCore or similar - Experience with managed or self-hosted ML infrastructure, such as Amazon SageMaker, feature stores (e.g. Feast) and DAG tooling (e.g. Airflow, Metaflow, or similar)
- Experience with managed or self-hosted ML infrastructure, such as Amazon SageMaker, feature stores (e.g. Feast) and DAG tooling (e.g. Airflow, Metaflow, or similar) - Experience with model experiment tracking, ideally MLflow or Weights & Biases, and with monitoring tools such as EvidentlyAI, CometML, Grafana or New Relic - Experience writing production-quality, tested Python code
- Experience productionising AI products, including agentic systems and the AIOps tooling behind them, ideally using AWS Bedrock AgentCore or similar - Experience with managed or self-hosted ML infrastructure, such as Amazon SageMaker, feature stores (e.g. Feast) and DAG tooling (e.g. Airflow, Metaflow, or similar) - Experience with model experiment tracking, ideally MLflow or Weights & Biases, and with monitoring tools such as EvidentlyAI, CometML, Grafana or New Relic

About Trainline

At Trainline, our purpose is to empower greener travel choices, connecting people and places.

In the employer’s words · Read in context · Company website ↗

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Actively hiringFirst seen by us 5 days ago, still listed, and not a repost of an earlier posting.

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Live
First seen by us
Oct 24, 2026 · 5 days ago
Recorded sightings
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Last seen by us
Oct 30, 2026

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Job description

View original posting ↗

About us

At Trainline, our purpose is to empower greener travel choices, connecting people and places. 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 🚄 

We’re Europe’s leading independent rail platform, helping millions of travellers find and book the best-value rail and coach journeys across our app, website and partner channels.

Our job is to make the green travel choice the best choice. By building a better train travel experience, we help more people choose rail - creating a positive impact for customers, our business and the planet.

We’re a team of more than 1,000 Trainliners from over 50 nationalities, working across London, Paris, Barcelona, Milan, Edinburgh and Madrid. Now is a brilliant time to join us and help shape the future of travel.

Introducing the Trainline MLOps Team 👋


Our MLOps team helps machine learning (ML) and AI teams across Trainline build, launch and run their products reliably. As a Senior MLOps Engineer, you will help shape how we operate AI and ML systems, including the infrastructure and tooling behind our internal ML/AI platforms and our customer-facing products. You will work on systems that are already being built and used across Trainline, so your impact will be real and visible. No two weeks look the same: one day you might be building out our ML platform, the next helping shape the architecture of our multi-agent AI products, and the next improving the developer experience with new tooling and standards. This is a hands-on role with technical leadership at its core. You will join an established team, working alongside our MLOps Engineering Manager and a small group of engineers, as we support a growing number of ML and AI teams. There is genuine scope to influence our technical direction, develop our engineering practices and mentor junior colleagues.


In this role as the Senior MLOps Engineer, you will... 🚅

  • Take a leading technical role in helping teams across Trainline adopt and operate ML and AI tooling as the technology evolves

  • Support AI infrastructure, working with the teams building our internal platform and customer-facing products, making sure products are well governed, monitored and deployed through automation

  • Build and drive adoption of one standard way to deploy ML and AI products, so that new products onboard quickly and reach production faster

  • Automate the modernisation of our services across products, including fixing and preventing security vulnerabilities in libraries and dependencies

  • Strengthen our MLOps foundations by improving cost tracking, finding ways to reduce costs, addressing security vulnerabilities and standardising engineering practices

  • Contribute to technical strategy, evaluating options and making well-reasoned decisions where there is no established approach

  • Communicate technical decisions clearly and help prioritise requests from the ML teams we support

  • Mentor fellow engineers and share knowledge through documentation and enablement across the organisation

We’d love to hear from you if you have...🔍

  • Hands-on experience with IaC and CI/CD tooling, ideally Terraform and GitHub Actions, though alternatives such as AWS CloudFormation and Jenkins are also relevant

  • Experience with containerised workloads using Docker and cloud platforms, ideally AWS, though GCP and Azure will be considered

  • Experience productionising AI products, including agentic systems and the AIOps tooling behind them, ideally using AWS Bedrock AgentCore or similar

  • Experience with managed or self-hosted ML infrastructure, such as Amazon SageMaker, feature stores (e.g. Feast) and DAG tooling (e.g. Airflow, Metaflow, or similar)

  • Experience with model experiment tracking, ideally MLflow or Weights & Biases, and with monitoring tools such as EvidentlyAI, CometML, Grafana or New Relic

  • Experience writing production-quality, tested Python code

  • Experience leading or contributing significantly to major platform changes and migrations, and the ability to lead technical work through ambiguity

  • Clear communication, sound prioritisation, and a track record of getting other teams to adopt shared standards and of mentoring colleagues

Nice to have's... 🔑

  • Knowledge of cloud topics such as networking fundamentals (VPC, security groups, CDN, WAF, etc.) and identity and access management (IAM, EntraID, SSO, etc.)

  • Experience with any of our wider tech stack and partners, including Spacelift, Braintrust and ElevenLabs.

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! 

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Location & working pattern

London, Greater London, United Kingdom

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:
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