Principal Machine Learning Engineer
London Stock Exchange Group2 months ago
Nottingham, United Kingdom or London, United KingdomStaff+
Responsibilities
- Define the end-to-end ML architecture for the matching platform, including data pipelines, training workflows, inference runtimes, telemetry, and monitoring.
- Lead AWS SageMaker adoption across training, processing, model registry, monitoring, deployment, retraining, approvals, and rollback orchestration.
- Architect scalable feature pipelines, feature-store standards, data-quality guardrails, validation schemas, and lakehouse-oriented feature architecture.
- Lead the design of ranking, scoring, similarity, calibration, confidence-threshold, and optimization strategies for matching models.
- Establish explainability standards and regulator-ready reason-code patterns using SHAP or equivalent frameworks.
- Design low-latency, high-throughput inference services, secure cross-account IAM patterns, telemetry, SLOs, and reliability strategies.
- Define monitoring for feature drift, concept drift, performance degradation, data integrity, privacy, and compliance.
- Set ML security and governance standards covering encryption, PII handling, model cards, lineage, auditability, reproducibility, versioning, and traceability.
- Lead validation and performance strategies using golden datasets, behavioral tests, benchmarks, A/B testing, shadow deployments, canary rollouts, and controlled experiments.
- Mentor engineers, review complex technical designs, influence product and architecture decisions, and set long-term engineering direction.
Requirements
- Proven experience architecting and delivering production machine learning systems at scale in enterprise environments.
- Deep expertise with AWS SageMaker, including training, processing, pipelines, endpoints, and model registry, plus complementary AWS services.
- Expert-level Python and experience with ML frameworks such as PyTorch, TensorFlow, and XGBoost.
- Strong expertise in MLOps automation, CI/CD for ML, model lifecycle management, and production deployment.
- Advanced experience designing explainability systems, reason codes, governance artifacts, drift detection, telemetry pipelines, observability, and model QA.
- Experience with low-latency inference architectures, real-time model serving, cross-account IAM, data minimization, and PII-safe design.
- Ability to influence architecture, mentor senior engineers, approve complex designs, and establish long-term technical standards.
- Experience with feature stores, ranking, search relevance, entity matching, similarity modeling, or multi-account AWS ML platforms is preferred.
- Knowledge of distributed training, GPU or accelerator optimization, and scaling strategies is preferred.
- Bachelor’s degree in a STEM subject such as mathematics, physics, engineering, or computer science is required; a master’s degree or PhD or equivalent STEM experience is desirable but not essential.
Benefits
- Healthcare, retirement planning, paid volunteering days, and wellbeing initiatives are offered.
- The company provides an inclusive, collaborative, and creative culture with equal-opportunity employment and reasonable accommodation support.
- Employees can participate in fundraising and volunteering through the LSEG Foundation.
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.