Lead Machine Learning Engineer, London Stock Exchange

• This role is suited to an experienced engineer who can deliver production‑grade ML systems, influence design decisions, and maintain high technical standards, while still working primarily as an individual contributor
• Strong experience delivering production ML systems end‑to‑end
• Proficiency with AWS SageMaker (training jobs, processing, endpoints, Model Registry)
• Excellent Python skills and experience with ML Models such as PyTorch, TensorFlow, or XGBoost
• Hands‑on experience with model explainability tools such as SHAP
• Understanding of low‑latency, real-time inference patterns and optimisation techniques
• Experience implementing drift detection, monitoring, and telemetry
• Working knowledge of ML governance, data privacy, and secure ML practices
• Strong understanding of MLOps, CI/CD, and automation for ML workflows

Desirable

• We are looking for a Lead Machine Learning Engineer (SageMaker, MLOps, Explainability) to design, build, and productionise machine learning models that power our new matching platform
• You will work across the full ML lifecycle—feature engineering, model development, training pipelines, deployment automation, inference optimisation, monitoring, and explainability
• In this role, you will make strong hands‑on technical contributions, take ownership of key components of the ML platform, and collaborate closely with data scientists, platform engineering, and product teams
• You will help improve our MLOps practices, enhance observability, and ensure that our ML systems meet standards for security, performance, and compliance
• Feature Engineering:
• Build and maintain scalable feature pipelines within data lakehouse architectures
• Develop fallback feature flows (e.g., export paths)
• Implement robust data quality checks and contribute to the adoption of feature store patterns
• Model Development & Scoring:
• Develop ranking, scoring, and entity‑similarity models fit for the matching platform
• Implement calibrated confidence scores, thresholds, and model scoring logic
• Use modern ML Model frameworks such as PyTorch, TensorFlow, or XGBoost
• Collaborate with data scientists on model design and performance improvements
• Explainability & Reason Codes:
• Apply SHAP or similar techniques to generate interpretable model explanations
• Produce reason codes suitable for business, operational, or regulatory consumption
• Ensure explainability outputs are versioned, tested, and integrated into inference workflows
• ML Deployment & MLOps:
• Build and maintain training, processing, and inference pipelines using AWS SageMaker
• Integrate models with model registries and implement automated deployment patterns
• Support rollback and redeploy mechanisms for weight updates or model iterations
• Contribute to CI/CD improvements for ML-specific workflows
• Inference Runtime & Cross‑Account Serving:
• Deploy and optimise low‑latency, real‑time inference endpoints
• Implement secure AWS IAM patterns supporting cross‑account inference access
• Build telemetry for request logging, performance tracking, and latency monitoring
• Solve runtime issues and optimise throughput and cost
• Monitoring, Drift Detection & Telemetry:
• Implement feature drift and concept drift monitoring
• Build dashboards, alerts, and critical performance metrics to detect model degradation
• Develop telemetry and logging frameworks that respect data minimisation principles
• Security, Compliance & ML Governance:
• Apply procedures for data handling, encryption, PII minimisation, and auditability
• Produce Model Cards, documentation, and lineage artefacts needed for governance
• Ensure that ML pipelines meet internal standards for reproducibility and traceability
• Testing, Validation & Performance:
• Conduct validation of models using golden datasets, baseline tests, and regression testing
• Optimise models for latency‑sensitive inference paths
• Support A/B tests, shadow deployments, and progressive rollout strategies