Solutions Engineer

Responsibilities

• Lead technical onboarding and deployment of complex AI/ML workloads for strategic enterprise customers
• Architect and deploy ML workloads using Kubernetes-based stacks such as Ray or Kubeflow
• Optimize AI/ML workloads directly on Crusoe infrastructure at the container and hardware level
• Assist customers in migrating and adapting workloads across AWS, Azure, and GCP
• Conduct technical workshops, live demos, and solution reviews
• Relay customer feedback to internal engineering and product teams to improve the platform

Requirements

• 3-5 years of experience building and deploying containerized workloads with Kubernetes
• Proficiency with Helm, Terraform, Docker, and multi-node orchestration
• Demonstrated success deploying ML frameworks like Ray, MLflow, or Airflow on Kubernetes
• Hands-on knowledge of cloud infrastructure including compute, storage, and networking in AWS, GCP, or Azure
• Strong Linux and CLI proficiency for troubleshooting infrastructure issues
• Experience in customer-facing technical roles navigating stakeholder conversations

Preferred Qualifications

• Experience with distributed ML orchestration platforms like Ray or Kubeflow
• Exposure to Slurm in a containerized MLOps context
• Multi-cloud deployment or migration experience
• Technical content contributions such as tech talks or blogs

About the Company

Crusoe is a vertically integrated AI infrastructure company that owns and operates each layer of the stack to power the world's most ambitious AI workloads using an energy-first approach.