Senior ML Infrastructure Engineer

Description

As a Senior ML Infrastructure Engineer, you will work directly in the Automation org with the core ML, Ops, and Analytics teams to help improve and build out the infrastructure around model deployment and monitoring. This role is essential to helping scale out the amount of time saving’s Gridware brings to customers.

• Develop monitoring and observability systems to track model performance, data drift, data quality, and overall system health.
• Design, build, and maintain the infrastructure, tooling, and workflows that enable reliable, scalable deployment of ML models to production.
• Create and maintain end-to-end testing frameworks and simulation environments to validate models and pipelines prior to deployment.
• Work closely with Data Engineering and Platform Engineering teams to ensure ML systems integrate cleanly with broader Gridware infrastructure and operational standards.
• Improve CI/CD pipelines for ML workloads, ensuring reproducibility, safe rollout, and automated rollback strategies.

Requirements

• 5+ years of experience building production ML infrastructure
• Familiarity with feature stores, model registries, or centralized metadata systems (i.e. MLFlow)
• Strong software engineering skills and proficiency in Python
• Experience with cloud platforms (AWS) and container orchestration (Kubernetes)

Benefits

Paid parental leave

Company-paid training

Commuter allowance

Alternating day off (every other Monday)

Health, Dental & Vision (Gold and Platinum with some providers plans fully covered)

“Off the Grid”, a two week per year paid break for all employees.

Training + Development

Interview process

Visa Sponsorship

Security clearance