Senior Lead Machine Learning Engineer

As Senior Lead ML Engineer, you will architect and deliver production-scale ML platforms that power a large-scale matching system. You will lead ML lifecycle governance, tooling, and operational excellence, working across data pipelines, model training, and deployment. You’ll build reliable, low-latency inference services and drive continuous improvement in ML workloads. This is a hands-on leadership role shaping how ML enables our financial market solutions at scale. You’ll collaborate with cross-functional teams to turn data into trusted, impactful models.

• healthcare
• retirement planning
• paid volunteering days
• wellbeing initiatives

• Define end-to-end ML architectures covering data pipelines, feature engineering, model training, deployment, inference, monitoring, and telemetry
• Set engineering standards and promote operational excellence across ML platforms
• Implement feature stores, lakehouse architectures, and data-quality frameworks
• Coach engineers, review designs, and steer technical direction
• Lead SageMaker-based workflows (pipelines, training, processing, model registry, endpoints) and related deployment practices
• Drive CI/CD, infrastructure as code, and automated model retraining
• Establish observability, drift detection, experimentation, and performance engineering for ML systems
• Manage multi-account AWS deployments and cross-account ML platform orchestration

• Enterprise-scale ML platform design and delivery, preferably with AWS SageMaker
• End-to-end ML solution delivery from data ingestion to production deployment and monitoring
• Experience building low-latency inference services and operating ML workloads at scale
• Proven ability to address scaling, reliability, and operational challenges in enterprise ML
• Deep hands-on expertise with SageMaker Pipelines, training, processing, model registry, endpoints, and monitoring
• Experience with CI/CD, IaC, model lifecycle management, and automated retraining
• Strong knowledge of model governance, explainability, traceability, auditability (SHAP, Model Cards) and documentation

• Coaching and mentoring of engineers
• Technical leadership and collaboration across teams
• Strong communication and design_review capabilities
• AWS SageMaker (Pipelines, Training, Processing, Registry, Endpoints, Monitoring)
• Feature stores and lakehouse architectures
• MLOps and deployment automation