ML Infrastructure Engineer

Responsibilities

• Design, build, and maintain the infrastructure, tooling, and workflows that enable reliable, scalable deployment of ML models to production.
• Develop monitoring and observability systems to track model performance, data drift, data quality, and overall system health.
• 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.

Required Skills

• 5+ years of experience building production ML infrastructure
• Strong software engineering skills and proficiency in Python
• Experience with cloud platforms (AWS) and container orchestration (Kubernetes)…