- 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
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 productionDemonstrable success designing and delivering enterprise‑scale ML platforms and products (ideally using AWS SageMaker)Experience building reliable, low‑latency inference services and operating ML workloads at scaleProven experience solving scaling, reliability, and operational challenges for enterprise ML systemsProven experience delivering ML solutions from data ingestion and feature engineering through to production deployment, monitoring, and continuous improvementBackground in ranking, search relevance, entity matching, or similarity modellingExperience in KYC, Sanctions Screening or Compliance domainsKnowledge of distributed training, GPU/accelerator optimisation, and scaling strategiesBachelors in a STEM subject, e.g. mathematics, physics, engineering, computer science, or adjacent degreesMasters or PhD or equivalent experience in STEM
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