Senior ML Infrastructure Engineer at Gridware responsible for ML infrastructure and deployment processes. Collaborating with core teams to enhance model monitoring and operational efficiency.
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.
Requirements
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)
Familiarity with feature stores, model registries, or centralized metadata systems (i.e. MLFlow)
Benefits
Health, Dental & Vision (Gold and Platinum with some providers plans fully covered)
Paid parental leave
Alternating day off (every other Monday)
“Off the Grid”, a two week per year paid break for all employees.
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