2+ years of hands-on experience building, deploying, or operating cloud infrastructure, with exposure to AI/ML, HPC, or GPU workloads
Hands-on proficiency with at least one major cloud provider (AWS, GCP, or Azure), deploying and managing infrastructure rather than just consuming it
Experience deploying containerized workloads with Kubernetes or Docker
Strong Linux command-line skills and scripting ability in Python or Bash
Networking fundamentals: VPCs, subnets, load balancers, DNS, routing
Clear technical communication, comfortable presenting demos and writing documentation customers actually use
Customer-facing instincts: curiosity about business problems, composure under questions, and a bias toward follow-through
Experience with distributed training or inference frameworks (PyTorch, Ray, Kubeflow)
Infrastructure-as-Code experience (Terraform, Ansible, CloudFormation)
GPU cluster, InfiniBand/RoCE, or Slurm exposure
Monitoring and observability tooling (Prometheus, Grafana, Datadog, CloudWatch)
Public technical content such as talks, blog posts, or how-to guides