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Crusoe Cloud is seeking a Senior+ Solutions Engineer to work closely with our most strategic enterprise customers deploying AI/ML workloads on Crusoe’s high-performance GPU infrastructure. This is a hands-on, customer-facing role requiring deep technical expertise in Kubernetes, MLOps, and cloud infrastructure. You’ll guide customers through end-to-end deployment—owning the PoC process, optimizing workloads post-sale, and serving as a critical technical voice between our customers and engineering teams. Ideal candidates are passionate about AI infrastructure, fluent in containerized environments, and confident translating workloads across cloud platforms.
Job Responsibility:
Customer Enablement: Lead technical onboarding and deployment of complex AI/ML workloads with strategic enterprise customers—owning the PoC through to post-sales optimization
Kubernetes + MLOps Focus: Architect and deploy ML workloads using Kubernetes-based stacks (e.g., Ray, Kubeflow) Design infrastructure that balances performance, scalability, and efficiency
Infrastructure-Centric Thinking: Go beyond abstracted services—deploy and optimize AI/ML workloads directly on Crusoe infrastructure. Ensure performance at the container and hardware level
Cross-Cloud Translation: Help customers migrate and adapt workloads across AWS, Azure, and GCP. Understand and explain the tradeoffs between cloud-native and Crusoe-native approaches
Technical Storytelling: Conduct workshops, live demos, and solution reviews. Contribute to case studies, solution briefs, and blog posts that highlight real-world customer success
Voice of the Customer: Relay feedback to internal engineering and product teams to continuously improve Crusoe’s platform based on real-world implementation experience
Requirements:
Deep Kubernetes Expertise: 3-5 years building and deploying containerized workloads
Experience with Helm, Terraform, Docker, and multi-node orchestration a must
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
Customer-Facing Technical Confidence: Able to navigate stakeholder conversations, gather requirements, lead technical engagements, and support customers in both pre- and post-sales environments
Strong Linux and CLI Proficiency: Comfortable operating in Linux environments and troubleshooting infrastructure issues via CLI
Collaborative Energy: Strong communication skills and eagerness to partner cross-functionally with Engineering, Product, and Sales to make customers successful
Must be able to pass a background check
Embody the Company values
Nice to have:
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)