Senior Lead Machine Learning Engineer
London Stock Exchange Group13 hours ago
London, United KingdomStaff+
Responsibilities
- Define end-to-end machine learning architectures spanning data ingestion, feature engineering, model training, deployment, inference, monitoring, and telemetry.
- Design and operate enterprise-scale ML platforms and production solutions, including reliable low-latency inference services and multi-account AWS deployments.
- Implement MLOps workflows covering SageMaker Pipelines, training, processing, model registries, endpoints, monitoring, deployment, CI/CD, infrastructure as code, and automated retraining.
- Establish engineering standards, operational excellence practices, feature stores, Lakehouse architectures, data quality frameworks, and model lifecycle processes.
- Develop and productionize machine learning solutions using techniques such as XGBoost and deep learning with PyTorch or TensorFlow.
- Create governance and explainability frameworks using traceability, auditability, SHAP, Model Cards, reproducibility, and model documentation.
- Lead validation and performance strategies using golden datasets, behavioral tests, benchmark suites, A/B testing, shadow deployments, canary rollouts, and controlled experiments.
- Coach engineers, review designs, solve scaling and reliability challenges, and drive technical direction.
Requirements
- Demonstrable success designing and delivering enterprise-scale machine learning platforms and products, ideally using AWS SageMaker.
- Proven experience taking ML solutions from data ingestion and feature engineering through production deployment, monitoring, and continuous improvement.
- Recent hands-on experience building, deploying, monitoring, and operating machine learning systems in production at scale.
- Strong experience with SageMaker Pipelines, Training, Processing, Model Registry, Endpoints, Monitoring, deployment workflows, and multi-account AWS environments.
- Deep knowledge of model governance, explainability, traceability, auditability, SHAP, Model Cards, and model documentation.
- Experience with scaling, reliability, operational challenges, performance engineering, and latency-sensitive inference paths.
- Experience establishing testing, validation, experimentation, shadow deployment, canary rollout, and controlled experiment standards.
- Bachelor’s degree in a STEM subject such as mathematics, physics, engineering, or computer science; a master’s degree, PhD, or equivalent experience is preferred.
- Experience in ranking, search relevance, entity matching, similarity modeling, KYC, sanctions screening, compliance, distributed training, GPU or accelerator optimization, and scaling strategies is desirable.
Benefits
- LSEG offers healthcare, retirement planning, paid volunteering days, wellbeing initiatives, and other tailored benefits and support.
- Employees can participate in fundraising and volunteering through the LSEG Foundation.
- The company provides a collaborative, creative, inclusive, and equal-opportunity work environment.
Categories
About London Stock Exchange Group
London Stock Exchange Group (LSEG) provides market infrastructure, financial data, and analytics to banks, asset managers, and corporations. Its businesses span capital markets (London Stock Exchange), post-trade clearing (LCH), and information services including FTSE Russell indices and the Refinitiv data platform, funded by transaction fees, subscriptions, and licensing. Headquartered in London and publicly listed on the LSE, the group serves global customers seeking trading, clearing, benchmarks, and enterprise data solutions.