Cloud AI work load orchestration engineer

About the Role

We're looking for an cloud orchestration engineer to build the operational backbone that keeps vLLM running reliably at massive scale. You'll design the systems for cluster management, deployment automation, and production monitoring that enable teams worldwide to serve AI models without friction. You'll ensure that vLLM deployments are observable, debuggable, and recoverable, turning operational complexity into infrastructure that just works.

Skills and Qualifications

Minimum qualifications:

Bachelor's degree or equivalent experience in computer science, engineering, or similar.

Strong experience with Kubernetes and container orchestration at scale.

Experience designing and implementing custom Kubernetes operators.

Proficiency in Python/Rust/Go and infrastructure-as-code tools (Terraform, Helm, etc).

Experience managing GPU clusters and debugging hardware issues.

Ability to work across cloud platforms (AWS, GCP, Azure) and on-premise infrastructure.

Preferred qualifications:

Experience with ML-specific orchestration tools (Ray, Slurm).

Knowledge of GPU scheduling, multi-tenancy, and resource optimization.

Familiarity with vLLM deployment patterns and configuration.

Track record of improving operational reliability for ML systems.

Bonus points if you have:

Experience deploying inference systems on large-scale GPU (1,000+) clusters.