Types of Projects and Impact: Take AI & ML applications from prototype to production, partnering closely with Data Scientists and cross-functional teams to ensure robust and performant deployment of machine learning solutions
Lead development for ML systems: Design, build, and maintain production-grade ML systems, with a focus on performance, scalability, and maintainability
Architect end-to-end ML infrastructure: Own the full lifecycle of ML solutions — from feature engineering and data pipelines to model serving, CI/CD, observability, and retraining
Collaborate across teams: Work closely with data scientists, data engineers, platform teams, and business stakeholders to deliver solutions that align with product and business needs
Champion MLOps best practices: Establish & maintain infrastructure/tooling for versioning, experimentation, testing, deployment, and monitoring of ML models
Enable reproducibility and scale: Develop reusable components, templates, and automation to scale ML development across use cases and teams
This role includes participation in a shared on-call rotation. The schedule will be communicated in advance, and we strive to balance coverage equitably while minimizing off-hours disruptions.
