Senior Solutions Engineer: AI Infra & Kubernetes

ProducePay; Denver (CO); On-site; USD 175,000 - 250,000; Full time

Benefits offered by this job

Restricted Stock Units

Health, dental & vision insurance

401(k) with company match

Paid time off

Job summary

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. You’ll guide customers through end-to-end deployment—from PoC through post-sale optimization—translating workloads across AWS, Azure, and GCP.

Qualifications

• 7+ years building and deploying containerized workloads.
• Experience with Helm, Terraform, Docker, and multi-node orchestration.
• Experience deploying ML frameworks (e.g., Ray, Kubeflow, MLflow) on Kubernetes.
• Cloud familiarity across AWS, GCP, or Azure.
• Strong customer-facing technical communication.
• Proficient in Linux and CLI.
• Ability to translate workloads across clouds.

Responsibilities

• Lead technical onboarding and deployment of complex AI/ML workloads with strategic enterprise customers.
• Architect and deploy ML workloads using Kubernetes-based stacks (Ray, Kubeflow); design scalable infrastructure.
• Deploy and optimize AI/ML workloads directly on Crusoe infrastructure.
• Help customers migrate workloads across AWS, Azure, and GCP.
• Conduct workshops, live demos, and solution reviews.
• Relay feedback to engineering and product teams to improve the platform.

Skills

Kubernetes

MLOps

Cloud infrastructure

Linux/CLI

Customer-facing

Terraform

Helm

Docker

Multi-cloud

Ray

Kubeflow

MLflow

Airflow

Tools

Kubeflow

Ray

MLflow

Airflow

Terraform

Docker

Job description

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. You’ll guide customers through end-to-end deployment—from PoC through post-sale optimization—translating workloads across AWS, Azure, and GCP.