Lead Machine Learning Engineer (ML Ops, SageMaker)

About the role

We are seeking a Lead Machine Learning Engineer to design, build and productionise ML models for our new matching platform. You will own key components of the ML platform, work across the full ML lifecycle and collaborate with data scientists, platform engineers and product teams.

Key responsibilities

• Design and maintain scalable feature pipelines within a data lakehouse architecture, including fallback flows and data quality checks.
• Develop ranking, scoring and entity‑similarity models using PyTorch, TensorFlow or XGBoost, and implement calibrated confidence scores.
• Apply SHAP or similar techniques to generate interpretable model explanations and reason codes for business and regulatory use.
• Build and maintain training, processing and inference pipelines on AWS SageMaker, integrating with model registries and automated deployment patterns.
• Implement secure cross‑account inference endpoints using AWS IAM, optimise low‑latency real‑time inference and monitor performance.
• Set up feature and concept drift monitoring, telemetry dashboards and CI/CD improvements for ML workflows.

Required profile

• Proven experience delivering production‑grade ML systems in a lead or senior engineering role.
• Strong hands‑on technical contribution ability with ownership of end‑to‑end ML components.
• Ability to work independently while collaborating closely with cross‑functional teams.

Required skills

• AWS SageMaker
• MLOps practices
• Feature engineering and data lakehouse concepts
• PyTorch
• TensorFlow
• XGBoost
• SHAP (or similar explainability tools)
• AWS IAM for cross‑account access
• CI/CD pipelines for ML