Senior Lead Machine Learning Engineer
Core
Lead the end-to-end machine learning lifecycle for a large-scale matching platform, defining architecture, engineering standards, and MLOps practices.
Role type
Senior Lead Machine Learning Engineer (Hands-on IC with technical leadership)
Builds
Production-scale ML systems, low-latency inference services, and enterprise ML platforms on AWS SageMaker.
Domain
Financial markets infrastructure and data
Deliverable
production ML models
Required skills
AWS SageMaker, MLOps, PyTorch, TensorFlow, XGBoost, model governance, explainability (SHAP), CI/CD, infrastructure as code, observability, drift detection, A/B testing, feature stores, Lakehouse architectures
Preferred skills
Ranking, search relevance, entity matching, similarity modelling, KYC, Sanctions Screening, distributed training, GPU/accelerator optimisation
Technologies
AWS SageMaker, PyTorch, TensorFlow, XGBoost
Responsibilities
Define end-to-end ML architectures spanning data pipelines, feature engineering, model training, deployment, inference, monitoring, and telemetry. Establish engineering standards and operational excellence. Implement feature stores, Lakehouse architectures, and enterprise data quality frameworks. Coach engineers, review designs, and drive technical direction. Lead validation strategies using golden datasets, behavioural tests, and benchmark suites. Architect performance testing for latency-sensitive inference paths and model hot paths.
Seniority
Senior, hands-on IC with technical leadership
