About the job
Crusoe Cloud is seeking a Sr. to Senior Staff level 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.
Responsibilities
- 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.
Qualifications
Minimum
- Deep Kubernetes Expertise: 7+ 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.
Preferred
- 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)