MLOps Engineering Manager

Trainline

MLOps Engineering Manager at Trainline leading a new team and shaping AI product delivery. Collaborating with multiple engineering and data teams to ensure operational excellence in machine learning.

Posted 7/9/2026full-timeLondon • 🇬🇧 United KingdomJunior💰 £100,000 - £115,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in leading MLOps teams and managing the deployment of machine learning products, with a strong focus on cloud infrastructure, DevOps practices, and effective communication across cross-functional teams.

Highest-signal resume keywords

MLOps Team LeadershipProductionising Machine Learning ModelsCloud Infrastructure (AWS)DevOps Technologies (Docker, Terraform, CI/CD)Strong Python Experience

ATS Keywords

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Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Machine Learning Development LifecycleMLOps Tools (MLflow, Airflow)Model MonitoringSparkPySpark

Soft Skills

Clear Communication Skills

Industry Keywords

Batch Machine Learning ModelsOnline Machine Learning ModelsExperimentationTestingContinuous Improvement

Tech Stack

Tools & technologies

AirflowAWSCloudDockerPySparkPythonSparkTerraform

About the role

Key responsibilities & impact

Build and lead a new team of MLOps Engineers
Define and evolve MLOps processes, tooling and infrastructure
Own the deployment and operation of machine learning products
Partner closely with engineering, data science, product and data teams
Support productionisation of batch and online machine learning models
Promote high standards for experimentation, testing, monitoring and continuous improvement
Contribute actively to Trainline’s AI and ML community
Help with thoughtful technology choices for long-term maintainability and measurable business value.

Requirements

What you’ll need

Experience leading, managing or mentoring engineers
Strong experience productionising machine learning models at scale
Good understanding of the machine learning development lifecycle
Experience with cloud infrastructure (ideally AWS) and DevOps technologies (Docker, Terraform, CI/CD)
Familiarity with MLOps tools and practices (MLflow, Airflow, model monitoring, etc.)
Strong Python experience, with knowledge of Spark or PySpark
Understanding of feature stores and related data technologies
Clear communication skills across engineering, data, product and business teams.

Benefits

Comp & perks

Private healthcare & dental insurance
Generous work from abroad policy
2-for-1 share purchase plans
EV Scheme to further reduce carbon emissions
Extra festive time off
Excellent family-friendly benefits
Career growth with clear paths, transparent pay bands, personal learning budgets, and regular learning days.