Principal Machine Learning Engineer

About Us

LSEG (London Stock Exchange Group) is a diversified global financial markets infrastructure and data business committed to delivering open‑access services that enable customers to pursue their ambitions with confidence and clarity.

We are seeking a Principal Machine Learning Engineer (Sage Maker, MLOps, Model Governance & Explainability) to provide technical leadership across the full lifecycle of machine learning systems powering a new matching platform. The role is accountable for defining ML architecture, establishing engineering standards, driving MLOps maturity, and ensuring that our models are scalable, secure, explainable, and governed to enterprise‑grade standards.

• Define the end‑to‑end ML architecture for the matching platform, including data pipelines, model training workflows, inference runtimes, and telemetry ecosystems.
• Lead adoption of best‑in‑class MLOps patterns, platform tooling, and AWS Sage Maker capabilities across training, processing, registry, monitoring and deployment.
• Partner with platform, security and data engineering teams to implement scalable lakehouse‑oriented feature architectures and enterprise‑grade ML governance.
• Champion engineering standards for model quality, documentation, observability and platform resilience.

• Architect highly scalable, production‑ready feature pipelines within lakehouse environments.
• Set the technical direction for fallback and resilience strategies.
• Establish and enforce data‑quality guardrails, validation schemas and monitoring frameworks.
• Drive adoption and standards for enterprise feature stores.

• Lead the design of ranking, scoring and similarity models tailored to the matching platform requirements.
• Define model calibration, scoring logic, confidence thresholds and optimisation strategies.
• Mentor teams on advanced ML techniques using frameworks such as PyTorch, Tensor Flow and XGBoost.
• Review and approve technical designs for complex modelling workflows.

• Establish explainability standards across the ML stack, using SHAP or equivalent frameworks.
• Define patterns to generate regulator‑ready reason codes aligned with compliance requirements.
• Ensure explainability artefacts are accurate, robust and traceable across model versions.

• Architect automated training, deployment and retraining pipelines using AWS Sage Maker.
• Set standards for model registry usage, automated approvals and rollback orchestration.
• Drive infrastructure‑as‑code and CI/CD maturity for ML systems across multiple environments.
• Lead design of enterprise‑wide weight‑update patterns and lineage‑aware deployment strategies.

• Architect low‑latency, high‑throughput inference services that meet strict matching platform SLAs.
• Lead the design of secure cross‑account IAM patterns for model consumption.
• Own end‑to‑end telemetry design, including scoring metrics, latency, error analytics and SLOs.
• Partner with platform teams to optimise cost, scale and reliability of inference endpoints.

• Define observability standards for feature drift, concept drift, performance degradation and data integrity.
• Lead the creation of dashboards, benchmarks and automated alerting across the ML ecosystem.
• Ensure telemetry pipelines adhere to privacy, data minimisation and compliance policies.
• Drive adoption of proactive failover, shadow‑mode testing and continuous validation patterns.

• Set and enforce ML‑specific security standards including data minimisation, encryption and PII handling.
• Oversee creation of model cards, lineage artefacts and compliance documentation.
• Ensure ML systems meet governance standards for auditability, reproducibility, versioning and traceability.
• Collaborate with Info Sec and Risk teams to define ML governance frameworks and secure cross‑environment workflows.

• Lead validation strategies using golden…