Principal Machine Learning Engineer (SageMaker, MLOps, Model Governance)

About the role

LSEG (London Stock Exchange Group) is looking for a Principal Machine Learning Engineer to lead the full lifecycle of machine learning systems powering a new matching platform. The role defines ML architecture, sets engineering standards, drives MLOps maturity and ensures models are scalable, secure, explainable and governed to enterprise standards. It is a highly hands-on, principal-level position combining architectural vision with deep practical expertise in ML engineering and AWS-native MLOps.

Key responsibilities

• Define the end-to-end ML architecture, including data pipelines, model training workflows, inference runtimes and telemetry, and lead adoption of MLOps patterns and AWS SageMaker.
• Partner with platform, security and data engineering teams on a scalable lakehouse-oriented feature architecture and enterprise ML governance.
• Build production-ready feature pipelines, data-quality guardrails, validation schemas and monitoring frameworks, and adopt enterprise feature stores.
• Lead the design of ranking, scoring and similarity models, including calibration, confidence thresholds and optimisation strategies, and mentor teams on PyTorch, TensorFlow and XGBoost.
• Set explainability standards using SHAP or equivalent, and define regulator-ready reason codes and traceable artefacts.
• Automate training, deployment and retraining pipelines with SageMaker, and drive infrastructure-as-code and CI/CD maturity for ML systems.
• Design low-latency inference services, secure cross-account IAM patterns, and telemetry covering scoring metrics, latency, errors and SLOs.
• Define observability for drift and performance, dashboards, alerts, shadow-mode testing and failover patterns.
• Enforce ML security standards (data minimisation, encryption, PII handling), Model Cards and governance documentation, working with InfoSec and Risk.
• Lead validation strategies, performance testing, A/B testing, shadow deployments and canary rollouts.

Required profile

• Proven track record architecting and delivering production ML systems at scale in enterprise environments.
• Deep expertise with AWS SageMaker and complementary AWS services.
• Expert-level Python and ML frameworks (PyTorch, TensorFlow, XGBoost).
• Thought leadership in MLOps automation, CI/CD for ML and model lifecycle management.
• Experience with explainability systems, reason codes, drift detection, telemetry pipelines, model QA and ML security (cross-account IAM, PII-safe design).
• Ability to influence architecture, mentor senior engineers and set long-term technical direction.