ML Infrastructure Engineer

Strategy:

• Own the long-term ML infrastructure roadmap and establish best practices for model lifecycle management.
• Identify infrastructure gaps and design scalable solutions to enable high-velocity ML development.
• Contribute to cross-functional technical planning to align ML systems with product strategy.

Technical Execution:

• Automate end-to-end ML lifecycle workflows including training, validation, deployment, and rollback.
• Implement robust monitoring for model performance, drift, and infrastructure health.
• Develop and maintain infrastructure-as-code to ensure scalable and secure cloud environments.

Team Collaboration:

• Serve as the technical bridge between ML experimentation and productized deployment.
• Share knowledge and best practices to elevate ML maturity across engineering and data science teams.
• Collaborate to support end-to-end ML workflows and translate data science needs into infrastructure solutions.