Senior Lead Machine Learning Engineer

Who you are

• We're looking for a Senior Lead ML Engineer with a proven track record of delivering successful, production-scale machine learning solutions (ideally on AWS SageMaker)
• This is a hands-on lead role requiring recent expertise in building, deploying, monitoring, and operating ML systems in production
• Demonstrable success designing and delivering enterprise-scale ML platforms and products (ideally using AWS SageMaker)
• Proven experience delivering ML solutions from data ingestion and feature engineering through to production deployment, monitoring, and continuous improvement
• Experience building reliable, low-latency inference services and operating ML workloads at scale
• Proven experience solving scaling, reliability, and operational challenges for enterprise ML systems
• Background in ranking, search relevance, entity matching, or similarity modelling
• Experience in KYC, Sanctions Screening or Compliance domains
• Knowledge of distributed training, GPU/accelerator optimisation, and scaling strategies
• Bachelors in a STEM subject, e.g. mathematics, physics, engineering, computer science, or adjacent degrees
• Masters or PhD or equivalent experience in STEM

What the job involves

• You will provide technical leadership across ML lifecycle, including defining architecture, engineering standards, MLOps practices, and governance frameworks for a large-scale matching platform
• Defining end-to-end ML architectures spanning data pipelines, feature engineering, model training, deployment, inference, monitoring, and telemetry
• Establishing engineering standards and operational excellence
• Implementing feature stores, Lakehouse architectures, and enterprise data quality frameworks
• Coaching engineers, reviewing designs, and driving technical direction
• Deep hands-on experience with SageMaker Pipelines, Training, Processing, Model Registry, Endpoints, Monitoring, and deployment workflows
• CI/CD, infrastructure as code, model lifecycle management, and automated retraining
• Observability, drift detection, experimentation, and performance engineering
• Multi-account AWS deployments and cross-account ML platforms
• Deep experience applying a wide range of machine learning techniques to real-world business problems, including using models such as XGBoost and deep learning approaches using frameworks such as PyTorch or TensorFlow, taking solutions from experimentation through to production deployment and operation
• Strong knowledge of model governance, explainability, traceability, auditability, SHAP, Model Cards, and model documentation practices
• Experience establishing governance frameworks that ensure models are explainable, reproducible, and compliant with enterprise standards
• Lead validation strategies using golden datasets, behavioural tests, and benchmark suites
• Architect performance testing for latency‑sensitive inference paths and model hot paths
• Establish standards for A/B testing, shadow deployments, canary rollouts, and controlled experiments