Senior Lead Machine Learning Engineer

London Stock Exchange Group; Greater London; Hybrid; GBP 110,000 - 180,000; Full time

Job summary

London Stock Exchange Group is seeking a Senior Lead ML Engineer to provide technical leadership across the ML lifecycle, from data pipelines to deployment, and to design scalable, production-ready ML platforms.

You will lead architecture, governance, and MLOps practices for a large-scale matching platform, mentoring engineers and driving governance and explainability across models.

Qualifications

• Proven track record delivering enterprise-scale ML platforms.
• Experience with AWS SageMaker and ML lifecycle orchestration.
• Hands-on building, deploying, monitoring and operating ML systems in production.
• Strong governance, explainability, traceability and model documentation practices.
• Leadership: coaching engineers and driving technical direction.

Responsibilities

• Provide technical leadership across ML lifecycle, including architecture, standards, and governance.
• Define end-to-end ML architectures spanning data pipelines, feature engineering, training, deployment and monitoring.
• Coach engineers, review designs and steer technical direction.
• Develop and operate production ML platforms with a focus on scale, reliability and observability.

Skills

AWS SageMaker

MLOps

Model governance

Explainability

PyTorch

TensorFlow

Education

Bachelor’s in STEM

Masters or PhD in STEM

Tools

SageMaker Pipelines

Lakehouse architectures

Job description

## Senior Lead Machine Learning EngineerApply: GBR-Nottingham-1 Chapel Qtr: London, United Kingdom: Full time: Posted Today: R0123436**About Us:**LSEG (London Stock Exchange Group) is more than a diversified global financial markets infrastructure and data business. We are dedicated, open-access partners with a dedication to excellence in delivering the services our customers expect from us. With extensive experience, deep knowledge and worldwide presence across financial markets, we enable businesses and economies around the world to fund innovation, manage risk and create jobs. It’s how we’ve contributed to supporting the financial stability and growth of communities and economies globally for more than 300 years. Through a comprehensive suite of trusted financial market infrastructure services – and our open-access model – we provide the flexibility, stability and trust that enable our customers to pursue their ambitions with confidence and clarity.LSEG is headquartered in the United Kingdom, with significant operations in 70 countries across EMEA, North America, Latin America and Asia Pacific. We employ 25,000 people globally, more than half located in Asia Pacific. LSEG’s ticker symbol is LSEG.**Our People:**People are at the heart of what we do and drive the success of our business. Our culture of connecting, creating opportunity and delivering excellence shape how we think, how we do things and how we help our people fulfil their potential. We embrace diversity and actively seek to attract individuals with unique backgrounds and perspectives. We break down barriers and encourage teamwork, enabling innovation and rapid development of solutions that make a difference. Our workplace generates an enriching and rewarding experience for our people and customers alike. Our vision is to build an inclusive culture in which everyone feels encouraged to fulfil their potential.We know that real personal growth cannot be achieved by simply climbing a career ladder – which is why we encourage and enable a wealth of avenues and interesting opportunities for everyone to broaden and deepen their skills and expertise. As a global organisation spanning 70 countries and one rooted in a culture of growth, opportunity, diversity **The Role:**We're looking for a Senior Lead ML Engineer with a **proven track record of delivering successful, production-scale machine learning solutions (ideally on AWS SageMaker)**.You will provide technical leadership across ML lifecycle, including defining architecture, engineering standards, MLOps practices, and governance frameworks for a large-scale matching platform. This is a hands-on lead role requiring recent expertise in building, deploying, monitoring, and operating ML systems in production.## Essential Experience* Demonstrable success designing and delivering enterprise-scale ML platforms and products (ideally using AWS SageMaker).* Proven experience delivering ML solutions from data ingestion and feature engineering through to production deployment, monitoring, and continuous improvement.* Experience building reliable, low-latency inference services and operating ML workloads at scale.* Proven experience solving scaling, reliability, and operational challenges for enterprise ML systems.Technical Leadership & Architecture* Defining end-to-end ML architectures spanning data pipelines, feature engineering, model training, deployment, inference, monitoring, and telemetry.* Establishing engineering standards and operational excellence.* Implementing feature stores, Lakehouse architectures, and enterprise data quality frameworks.* Coaching engineers, reviewing designs, and driving technical direction.MLOps, Deployment & Operations* Deep hands-on experience with SageMaker Pipelines, Training, Processing, Model Registry, Endpoints, Monitoring, and deployment workflows.* CI/CD, infrastructure as code, model lifecycle management, and automated retraining.* Observability, drift detection, experimentation, and performance engineering.* Multi-account AWS deployments and cross-account ML platforms.Model Development, Governance & Explainability* Deep experience applying a wide range of machine learning techniques to real-world business problems, including using models such as XGBoost and deep learning approaches using frameworks such as PyTorch or TensorFlow, taking solutions from experimentation through to production deployment and operation.* Strong knowledge of model governance, explainability, traceability, auditability, SHAP, Model Cards, and model documentation practices.* Experience establishing governance frameworks that ensure models are explainable, reproducible, and compliant with enterprise standards.Testing, Validation & Performance Engineering* Lead validation strategies using golden datasets, behavioural tests, and benchmark suites.* Architect performance testing for latency‐sensitive inference paths and model hot paths.* Establish standards for A/B testing, shadow deployments, canary rollouts, and controlled experiments.## Nice to Have* Background in ranking, search relevance, entity matching, or similarity modelling.* Experience in KYC, Sanctions Screening or Compliance domains.* Knowledge of distributed training, GPU/accelerator optimisation, and scaling strategies.* Bachelors in a STEM subject, e.g. mathematics, physics, engineering, computer science, or adjacent degrees.* Masters or PhD or equivalent experience in STEM.**Career Stage:**Manager