🛠 Tech Stack

💼 About This Role

You'll guide strategic enterprise customers deploying AI/ML workloads on Crusoe's high-performance GPU infrastructure, owning the PoC process and optimizing workloads post-sale. As the primary technical voice, you'll translate customer needs to internal engineering teams. This role offers a chance to work on cutting-edge containerized MLOps in a vertically integrated AI infrastructure company.

🎯 What You'll Do

• Lead technical onboarding and deployment of AI/ML workloads for strategic enterprise customers.
• Architect and deploy ML workloads using Kubernetes-based stacks (e.g., Ray, Kubeflow).
• Help customers migrate and adapt workloads across AWS, Azure, and GCP.
• Conduct workshops, live demos, and solution reviews.

📋 Requirements

• Kubernetes expertise: 7+ years building and deploying containerized workloads.
• MLOps deployment experience with frameworks like Ray, MLflow, or Airflow on Kubernetes.
• Hands-on cloud infrastructure knowledge (AWS, GCP, or Azure).
• Strong Linux and CLI proficiency.

✨ Nice to Have

• Experience with Ray, Kubeflow, or distributed ML orchestration platforms.
• Exposure to Slurm with focus on containerized MLOps.
• Multi-cloud deployment or migration experience.

🎁 Benefits & Perks

• 💰 Competitive compensation and Restricted Stock Units.
• 🏖️ Paid time off and paid holidays.
• 🏥 Comprehensive health, dental & vision insurance with HSA employer contributions.
• 👶 Paid parental leave.
• 📈 401(k) Retirement plan with company match up to 4%.