Principal Machine Learning Engineer
LSEG (London Stock Exchange Group)
full-time
Posted on:
Location Type: Office
Location: London • United Kingdom
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Job Level
Tech Stack
About the role
- Define the end‑to‑end ML architecture for the matching platform, including data pipelines, model training workflows, inference runtimes, and telemetry ecosystems.
- Lead adoption of best‑in‑class 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 architectures and enterprise‑grade ML governance.
- Champion engineering standards for model quality, documentation, observability, and platform resilience.
- Architect highly scalable, production‑ready feature pipelines within Lakehouse environments.
- Lead the design of ranking, scoring, and similarity models tailored to the matching platform requirements.
- Ensure explainability artefacts are accurate, robust, and traceable across model versions.
- Architect automated training, deployment, and retraining pipelines using AWS SageMaker.
- Define observability standards for feature drift, concept drift, performance degradation, and data integrity.
Requirements
- Proven track record architecting and delivering production ML systems at scale in enterprise environments.
- Deep expertise with AWS SageMaker (training, processing, pipelines, endpoints, registry) and complementary AWS services.
- Expert-level Python and ML Model frameworks (e.g. PyTorch, TensorFlow, XGBoost).
- Strong thought leadership in MLOps automation, CI/CD for ML, and model lifecycle management.
- Advanced experience designing explainability systems, reason codes, and governance artefacts.
- Expertise in low‑latency inference architectures and real-time model serving.
- Strong grounding in drift detection, telemetry pipelines, observability patterns, and model QA.
- Experience shaping ML security practices, including cross‑account IAM, data minimisation, and PII-safe design.
- Ability to influence architecture, mentor senior engineers, and set long-term technical direction.
Benefits
- Healthcare
- Retirement planning
- Paid volunteering days
- Wellbeing initiatives
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
ML architecturedata pipelinesmodel training workflowsinference runtimesMLOpsPythonPyTorchTensorFlowXGBoostdrift detection
Soft Skills
thought leadershipinfluence architecturementoringsetting technical direction