Principal ML Architect – SageMaker, MLOps & Governance

LSEG; Greater London; On-site; GBP 120,000 - 180,000; Full time

Benefits offered by this job

Healthcare

Retirement planning

Volunteer days

Wellbeing initiatives

Job summary

LSEG is seeking a Principal Machine Learning Engineer to provide technical leadership across the full ML lifecycle for a new matching platform. You will define ML architecture, establish engineering standards, and drive MLOps maturity across training, processing, registry, monitoring and deployment.

You will partner with platform, security and data engineering teams to build scalable feature architectures in Lakehouse environments, enforce governance and explainability standards, and design

Qualifications

• Proven track record architecting production ML systems at scale.
• Deep expertise with AWS SageMaker and related AWS services.
• Expert-level Python and ML frameworks (PyTorch, TensorFlow, XGBoost).
• Strong leadership in MLOps automation and model lifecycle management.
• Experience designing explainability systems, reason codes, and governance artefacts.
• Expertise in low-latency inference architectures and real-time model serving.
• Shaping ML security practices, including cross-account IAM and data minimisation.
• Ability to influence architecture and mentor senior engineers.

Responsibilities

• Define the end-to-end ML architecture for the platform, including data pipelines, training workflows, inference runtimes and telemetry ecosystems.
• Lead adoption of MLOps patterns, platform tooling, and AWS SageMaker capabilities across training, processing, registry, monitoring and deployment.
• Partner with platform, security and data engineering teams to implement scalable data lakehouse oriented feature architecture and governance.
• Champion engineering standards for model quality, documentation, observability, and platform resilience.
• Architect scalable feature pipelines within Lakehouse environments and establish fallback strategies.
• Set data-quality guardrails, validation schemas, and monitoring frameworks for models.
• Drive enterprise feature stores adoption and standards; design ranking, scoring, and similarity models.
• Define calibration, scoring logic, confidence thresholds, and optimisation strategies.
• Mentor teams on advanced ML techniques using PyTorch, TensorFlow, and XGBoost.
• Review and approve technical designs for complex modeling workflows and explainability artefacts.
• Establish explainability standards across the ML stack with SHAP or equivalents.
• 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 SageMaker.
• Set standards for model registry usage, automated approvals, and rollback orchestration.
• Drive IaC and CI/CD maturity for ML systems across environments.
• Lead weight-update patterns and lineage-aware deployment strategies.
• Architect low-latency, high-throughput inference services meeting SLAs.
• Lead secure cross-account IAM patterns for model consumption.
• Own 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 drift, performance degradation and data integrity.
• Lead dashboards, benchmarks and automated alerting across the ML ecosystem.

Skills

AWS SageMaker

Python

ML frameworks

MLOps automation

CI/CD for ML

Explainability

Model governance

Telemetry design

Leadership

Cross-team collaboration

Education

Bachelor's degree in STEM

Master's or PhD in STEM desirable

Tools

PyTorch

TensorFlow

XGBoost

AWS services

CI/CD tools

Job description

LSEG is seeking a Principal Machine Learning Engineer to provide technical leadership across the full ML lifecycle for a new matching platform. You will define ML architecture, establish engineering standards, and drive MLOps maturity across training, processing, registry, monitoring and deployment.

You will partner with platform, security and data engineering teams to build scalable feature architectures in Lakehouse environments, enforce governance and explainability standards, and design