Senior Lead Machine Learning Engineer

Description

The company is a global provider of financial markets infrastructure and data. It is headquartered in the UK and has significant operations in 70 countries across EMEA, North America, Latin America and Asia Pacific.

It has been operating for more than 300 years and employs 25,000 people, over half of them in Asia Pacific. The company describes its workforce as spanning 65 countries.

The company is committed to sustainability and works with customers on their sustainability goals. Its charity, the company Foundation, funds community groups that help people gain economic opportunities. Colleagues can take part through fundraising and volunteering.

The team works on a large-scale matching platform and values new ideas, with plenty of room to build new skills across a wide range of projects.

Responsibilities

• Set the technical direction for the full ML lifecycle, covering architecture, engineering standards, MLOps and governance.
• Define end-to-end ML architectures across data pipelines, feature engineering, training, deployment, inference, monitoring and telemetry.
• Build feature stores, Lakehouse architectures and data quality frameworks that hold up across the enterprise.
• Set operational standards and drive operational excellence across the ML estate.
• Design validation using golden datasets, behavioural tests and benchmark suites.
• Architect performance testing for latency-sensitive inference paths and model hot paths.
• Define standards for A/B tests, shadow deployments, canary rollouts and controlled experiments.
• Stay hands-on with building, deploying, monitoring and running ML systems in production.
• Coach engineers, review their designs and guide technical direction.

Requirements

• A proven record of delivering production-scale ML solutions, ideally on AWS SageMaker.
• Experience designing and delivering enterprise-scale ML platforms and products, ideally on SageMaker.
• A track record of taking ML work from data ingestion and feature engineering through to deployment, monitoring and ongoing improvement.
• Experience building reliable, low-latency inference services and running ML workloads at scale.
• A history of solving scaling, reliability and operational problems in enterprise ML systems.
• Deep hands-on experience with SageMaker Pipelines, Training, Processing, Model Registry, Endpoints, Monitoring and deployment workflows.
• Experience with CI/CD, infrastructure as code, model lifecycle management and automated retraining.
• Experience with observability, drift detection, experimentation and performance engineering.
• Experience with multi-account AWS deployments and cross-account ML platforms.
• Deep experience applying many machine learning techniques to business problems, including XGBoost and deep learning with PyTorch or TensorFlow, from experiments to production.
• Strong knowledge of model governance, explainability, traceability, auditability, SHAP, Model Cards and model documentation.
• Experience setting up governance frameworks that keep models explainable, reproducible and compliant with enterprise standards.
• Background in ranking, search relevance, entity matching or similarity modelling.
• Experience in KYC, sanctions screening or compliance domains.
• Knowledge of distributed training, GPU or accelerator optimisation and scaling strategies.
• A bachelor's degree in a STEM subject such as mathematics, physics, engineering, computer science or an adjacent field.
• A master's, PhD or equivalent experience in STEM.

Conditions

This is a manager-level role.

Equal opportunities employer: no discrimination on protected grounds, with reasonable accommodation for religious practice, mental health and disability needs.

Benefits

• Tailored benefits and support, including healthcare, retirement planning, wellbeing initiatives and paid volunteering days.