Solutions Engineer

Join Crusoe Cloud as a Senior Solutions Engineer, where you'll work closely with strategic enterprise customers deploying AI/ML workloads on our high-performance GPU infrastructure. This hands-on, customer-facing role requires deep technical expertise in Kubernetes, MLOps, and cloud infrastructure. You'll guide customers through end-to-end deployment, optimize workloads post-sale, and serve as a critical technical voice between our customers and engineering teams.

• Lead technical onboarding and deployment of complex AI/ML workloads with strategic enterprise customers, owning the PoC through to post-sales optimization.
• Architect and deploy ML workloads using Kubernetes-based stacks (e.g., Ray, Kubeflow) and design infrastructure that balances performance, scalability, and efficiency.
• Conduct workshops, live demos, and solution reviews, and contribute to case studies, solution briefs, and blog posts that highlight real-world customer success.
• Ideal candidates are passionate about AI infrastructure, fluent in containerized environments, and confident translating workloads across cloud platforms
• Strong Linux and CLI Proficiency:Comfortable operating in Linux environments and troubleshooting infrastructure issues via CLI
• Customer-Facing Technical Confidence: Able to navigate stakeholder conversations, gather requirements, lead technical engagements, and support customers in both pre- and post-sales environments
• MLOps Deployment Experience: Demonstrated success deploying ML frameworks (e.g., Ray, MLflow, Airflow) on Kubernetes—especially for inference and model training workflows
• Hands-on Cloud Infrastructure Knowledge:Familiarity with compute, storage, networking, and scaling in AWS, GCP, or Azure. Experience translating workloads across clouds is highly desirable
• Collaborative Energy: Strong communication skills and eagerness to partner cross-functionally with Engineering, Product, and Sales to make customers successful
• Deep Kubernetes Expertise: 3-5 years building and deploying containerized workloads. Experience with Helm, Terraform, Docker, and multi-node orchestration a must
• Experience with Ray, Kubeflow, or other distributed ML orchestration platforms
• Exposure to Slurm, but with a primary focus on containerized MLOps over traditional HPC
• Multi-cloud deployment or migration experience (especially AWS ➝ Crusoe transitions)
• Content contributions (tech talks, blogs, public case studies
• Must be able to pass a background check
• Embody the Company values