ML Engineering Manager: Lead Production AI & Pipelines

Trainline; Greater London; Hybrid; GBP 110,000 - 150,000; Full time

Benefits offered by this job

Private healthcare

Dental insurance

Work from abroad policy

Share purchase plan

EV scheme

Extra festive time off

Family‑friendly benefits

Job summary

Trainline is hiring a Machine Learning Engineering Manager to lead a team of 7 ML and data engineers within the Core Experience pillar (Search, Supply, Commerce). You’ll own delivery quality, architecture decisions, and data pipelines while collaborating with data scientists, software engineers and product managers to drive measurable customer outcomes.

You'll shape technical direction, oversee end-to-end ML systems, and ensure robust evaluation, monitoring, and governance.

Qualifications

• Experience line-managing engineers, incl. hiring and performance management.

Responsibilities

• Lead, coach and grow a team of ML and data engineers.
• Own delivery: plan and prioritise with product partners across Search, Supply and Commerce.
• Set the technical direction for the team’s systems and assess build-vs-buy decisions.
• Ensure reliable data pipelines, features, and robust evaluation including A/B testing.
• Lead the creation of production-grade agentic systems with LangGraph and tool integration.
• Establish evaluation as a discipline with logs, tracing and monitoring in production.
• Set guardrails for safe, responsible AI use and governance.
• Build strong relationships with senior leaders and cross-functional partners.

Skills

People management

ML engineering

Python

AWS

LLMs

A/B testing

Data pipelines

Stakeholder management

Education

Bachelor's degree in Computer Science or related field

Tools

LangGraph

Airflow

MLflow

Docker

ECS

Terraform

Job description

Trainline is hiring a Machine Learning Engineering Manager to lead a team of 7 ML and data engineers within the Core Experience pillar (Search, Supply, Commerce). You’ll own delivery quality, architecture decisions, and data pipelines while collaborating with data scientists, software engineers and product managers to drive measurable customer outcomes.

You'll shape technical direction, oversee end-to-end ML systems, and ensure robust evaluation, monitoring, and governance.