Principal Machine Learning Engineer

Overview

In this role, you will lead the end-to-end ML platform for a new matching system, shaping architecture and governance for scalable, secure ML solutions. You’ll drive MLOps maturity, mentor engineers, and partner across platform, security and data teams to deliver enterprise-grade ML capabilities. This is a hands-on, senior role combining architecture with deep ML engineering expertise, focused on reliable, explainable models. You will help define the strategic ML platform direction and ensure compliant, high‑quality deployments.

Pay / Benefits

• healthcare
• retirement planning
• paid volunteering days
• wellbeing initiatives
• charitable involvement via LSEG Foundation

Responsibilities

• Define end-to-end ML architecture for the matching platform, including data pipelines, training workflows and telemetry
• Lead adoption of MLOps patterns and AWS SageMaker across training, processing, registry, monitoring and deployment
• Collaborate with platform, security and data engineering teams on scalable feature architectures and governance
• Set engineering standards for model quality, observability and resilience
• Architect scalable, production-ready feature pipelines in Lakehouse environments
• Define resilience strategies and guardrails for data quality and monitoring
• Drive enterprise feature stores adoption and governance for ML features
• Lead design of ranking, scoring and similarity models for the platform
• Define model calibration, scoring logic and optimization strategies
• Mentor teams on ML techniques using PyTorch, TensorFlow and XGBoost
• Review and approve designs for complex modeling workflows
• Establish explainability standards and regulator-ready reason codes
• Architect automated training, deployment and retraining pipelines using SageMaker
• Set standards for model registry usage, approvals and rollback orchestration
• Lead CI/CD maturity for ML systems and cross-environment workflows
• Design cross-account, low-latency inference services and telemetry schemas
• Define observability for drift, performance and data integrity
• Create dashboards, benchmarks and automated alerts for ML ecosystem
• Enforce ML security, data minimisation and PII handling
• Oversee model cards, lineage artefacts and compliance documentation
• Coordinate with InfoSec and Risk teams on governance frameworks
• Lead validation with golden datasets and benchmark suites
• Design performance tests for latency-sensitive paths and experiments

Key requirements

• Proven track record architecting and delivering production ML systems at scale in enterprise environments
• Deep expertise with AWS SageMaker and related services
• Expert Python and ML frameworks (PyTorch, TensorFlow, XGBoost)
• Strong MLOps automation, CI/CD for ML and lifecycle management
• Advanced explainability systems, reason codes and governance artefacts
• Low-latency, real-time inference architectures
• Drift telemetry, observability and model QA expertise
• ML security practices including cross-account IAM and PII-safe design
• Ability to influence architecture, mentor senior engineers and set long-term direction

• Thought leadership
• Mentorship and collaboration
• Strong communication across teams
• AWS SageMaker (training, processing, pipelines, endpoints, registry)
• Python
• PyTorch

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