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MLOps Engineering Manager
TrainlineMLOps 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.
Tech Stack
Tools & technologiesAirflowAWSCloudDockerPySparkPythonSparkTerraform
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.
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
Machine Learning Development LifecycleMLOps Tools (MLflow, Airflow)Model MonitoringSparkPySpark
Soft Skills
Clear Communication Skills