Role overview
We are seeking a highly skilled AI Engineer to serve as a core builder of our AI Data Platform. This role sits at the intersection of machine learning engineering, data platform development, and business intelligence, with responsibility for designing and operating the infrastructure that powers AI-driven insights across the organization.
You will build intelligent data pipelines, production-grade ML systems, and AI-enabled features that translate complex data into actionable outcomes. This role is ideal for an engineer who enjoys working end-to-end from data ingestion and feature engineering to model deployment and downstream consumption in analytics and BI tools.
Applicants must be authorized to work for ANY employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.
What you'll work on
- Build AI-first data pipelines: Design, implement, and maintain scalable data pipelines that support model training, inference, and analytics use cases across the AI Data Platform.
- Deploy production ML systems: Develop, deploy, and monitor machine learning models using AWS SageMaker, ensuring reliability, observability, and performance in production environments.
- Implement Retrieval-Augmented Generation (RAG): Architect and maintain RAG-based systems that combine structured and unstructured data to power AI-driven insights and applications.
- Operationalize ML lifecycle management: Use MLflow for experiment tracking, model versioning, and lifecycle management to support reproducibility and continuous improvement.
- Design feature infrastructure: Build and manage feature stores (e.g., Feast, Tecton, or SageMaker Feature Store) to ensure consistent, reusable features across training and inference.
- Orchestrate complex workflows: Create and manage Apache Airflow DAGs to orchestrate data transformations, model pipelines, and AI workflows with clear dependencies and monitoring.
- Enable analytics consumption: Partner with BI and analytics teams to ensure ML outputs integrate cleanly with our internal BI reporting hub.
- Translate business questions into AI solutions: Collaborate with stakeholders to convert ambiguous business problems into measurable ML- and data-driven solutions.
- Uphold data quality and governance: Ensure AI pipelines and models adhere to data governance, security, and quality standards, particularly when handling sensitive data.
- Collaborate cross-functionally: Work closely with Data Science, Analytics Engineering, Medical Economics, and DataOps to align AI platform capabilities with business priorities.