Platform Engineer

About the Role

Highly skilled AI Infrastructure Engineer to design, build, and operate scalable GPU-enabled Kubernetes platforms for AI/ML workloads.

Requirements

• 3-8+ years' experience
• Strong Kubernetes knowledge
• Experience with GPUs and NVIDIA stack
• Linux (Ubuntu) expertise
• Experience with Terraform

Preferred Qualifications

• Longhorn or Ceph experience
• Canonical ecosystem (MAAS, Juju)
• AI/ML tools like Kubeflow
• Certifications (CKA, NVIDIA)

Skills

• Strong problem-solving and troubleshooting mindset
• Ability to collaborate with cross-functional teams (ML engineers, data scientists)
• Clear communication and documentation skills
• Passion for automation and platform scalability

Responsibilities

• Design and manage Kubernetes clusters
• Build GPU-enabled infrastructure
• Deploy Longhorn storage
• Automate infrastructure using Terraform
• Monitor systems using Prometheus and Grafana
• Provide structured knowledge transfer (KT) sessions to client teams on all core platform components, including:
  • Kubernetes architecture, operations, and troubleshooting
  • GPU infrastructure (NVIDIA stack, scheduling, resource optimization)
  • Longhorn storage management and performance tuning
  • Canonical ecosystem tools (MAAS, Juju, Charmed Kubernetes)
• Develop and deliver technical documentation, runbooks, and training materials to support ongoing operations
• Conduct hands-on workshops and guided sessions to enable client teams to independently manage and scale the platform
• Act as a technical advisor, helping client stakeholders understand best practices in:
  • Cloud-native infrastructure
  • AI/ML platform operations
  • Reliability, performance, and cost optimization
• Ensure smooth handoff of production systems with full operational readiness and support knowledge

Duration: 160 hours
Location: St. Louis, MO (100% onsite)
Note: US Citizen or Green Card candidates only