We are expanding our team with a Data & ML Infrastructure Engineer who will take ownership of the platform that enables data ingestion, processing, storage, and machine learning workflows across multiple products. Operating at the intersection of data science, software engineering, and cloud infrastructure, you will build the foundations and ensure that our ML systems are scalable, reproducible, and production ready.
In this role, you will design, implement, and continuously improve a cloud-agnostic data and ML platform that supports multiple products and teams. You will be responsible for platform reliability, scalability, and cost-efficiency, while providing data scientists and ML engineers with the tools and infrastructure to efficiently develop, train, validate, and deploy machine learning models. You will serve as a technical bridge between the data science and IT/infrastructure teams, translating requirements into robust and maintainable systems.
This role includes ownership of an existing vendor-delivered platform and its evolution into a fully automated, reproducible, and multi-product ML platform.
Key Responsibilities:
Design, implement, and maintain the data and ML platform infrastructure, including systems for data ingestion, storage, processing, and training.
Ensure high availability, reliability, and uptime of the platform, as it is critical to the business value proposition.
Maintain and evolve vendor-delivered platform components and ensure long-term maintainability and independence.
Build and maintain data workflows, including tools for dataset versioning, experiment tracking, and model lifecycle management across multiple products.
Enforce DevOps / MLOps practices, including CI/CD pipelines, Infrastructure-as-Code, and automated workflows.
Develop…