Senior Lead ML Engineer — Enterprise-Scale, AWS SageMaker

London Stock Exchange Group; Nottingham; On-site; GBP 90,000 - 150,000; Full time

Benefits offered by this job

Healthcare

Retirement planning

Paid volunteering days

Wellbeing initiatives

Job summary

London Stock Exchange Group is seeking a Senior Lead ML Engineer to provide technical leadership across the ML lifecycle, from data ingestion and feature engineering to production deployment and monitoring. The role focuses on building scalable, low-latency ML systems and establishing robust governance and MLOps practices.

The successful candidate will lead design and implementation of end-to-end ML architectures, coach engineers, and ensure alignment with enterprise standards, while delivering

Qualifications

• Deep experience designing and delivering enterprise-scale ML platforms and products (ideally with AWS SageMaker).
• Proven ability to take ML projects from data prep to production deployment and monitoring.
• Strong background in building reliable, low-latency inference services at scale.

Responsibilities

• Lead design of end-to-end ML architectures spanning data pipelines, model training, deployment, and monitoring.
• Establish engineering standards and operational excellence across ML lifecycles.
• Coach engineers, review designs, and drive technical direction for production ML systems.
• Implement governance for explainability, traceability, and compliance.

Skills

AWS SageMaker

ML lifecycle

MLOps

CI/CD

Observability

Model governance

Explainability

Low-latency inference

PyTorch

TensorFlow

Education

Bachelor's in STEM

Masters/PhD in STEM

Tools

SageMaker Pipelines

Model Registry

Lakehouse architectures

Feature stores

Job description

London Stock Exchange Group is seeking a Senior Lead ML Engineer to provide technical leadership across the ML lifecycle, from data ingestion and feature engineering to production deployment and monitoring. The role focuses on building scalable, low-latency ML systems and establishing robust governance and MLOps practices.

The successful candidate will lead design and implementation of end-to-end ML architectures, coach engineers, and ensure alignment with enterprise standards, while delivering