Desirable Experience
Networking: Experience with high-speed interconnects (InfiniBand NDR/HDR, RoCEv2) and debugging connectivity issues.
Containerisation: Experience with Docker and Kubernetes (specifically the NVIDIA GPU Operator).
Cisco Integration: Familiarity with Cisco UCS or Cisco Nexus configurations in an AI context.
Certifications
Highly Desirable:
NVIDIA-Certified Associate: AI Infrastructure and Operations (NCA-AIIO)
NVIDIA-Certified Professional: AI Infrastructure (NCP-AII)
Red Hat Certified System Administrator (RHCSA)
Success Metrics (KPIs)
Deployment Velocity: Achieving
Validation Accuracy: 100% of handed-over nodes pass the "Golden Config" validation script (zero "Dead-on-Arrival" nodes handed to the client).
Ticket Efficiency: Consistently meeting SLAs for L2 support tickets.
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Lead the build-out of an enterprise AI platform for a global enterprise client, designing the foundation for teams to ship LLM applications and agents including Kubernetes, GPU capacity, and security.
Compensation
The Denver base salary range for this role is $180,000 - $220,000.
Responsibilities
Design and build a multi-tenant platform for LLM and agentic AI workloads, from model access to deployment
Architect GPU infrastructure for inference and fine-tuning, including scheduling, autoscaling, and cost controls
Stand up shared services for agents: model gateways, secrets, tracing, evaluation, and deployment pipelines
Make build-vs-buy calls and explain the tradeoffs to technical and executive stakeholders
Architect and run production Kubernetes on Azure AKS and GCP GKE
Essential Skills
HPC & AI Infrastructure:
Expert-level knowledge of NVIDIA Base Command Manager (BCM) or Metal-as-a-Service (MaaS) provisioning tools.
Deep understanding of Slurm configuration (cgroups, plugin development, accounting).
Proficiency with NVIDIA DGX/HGX hardware architecture and the associated software stack (Drivers, CUDA, DCGM).
Linux & Automation (DevOps for Hardware):
Mastery of Red Hat Enterprise Linux (RHEL) / Ubuntu internals (Systemd, Kernel Tuning, Hugepages).
Advanced proficiency in Ansible (writing custom modules/roles) and Python (automating admin tasks).
Experience with Git workflows (Branching, PRs, CI/CD).
Containerisation:
Hands-on experience with Docker, Singularity/Apptainer, and Kubernetes (specifically NVIDIA GPU Operator and NVIDIA Network Operator).
Desirable Experience
Network Awareness: Ability to troubleshoot basic InfiniBand/RoCEv2 issues (ibstat, perf query) to distinguish between a "Node Issue" and a "Network Issue."
Storage Integration: Experience mounting high-performance parallel file systems (VAST/Lustre/WEKA/GPFS) and tuning client-side performance.
Certifications:
NVIDIA Certified Associate - AI in the Data Center.
Red Hat Certified Engineer (RHCE).
CKA (Certified Kubernetes Administrator).
Success Metrics (KPIs)
Deployment Velocity: Reduction in "Time-to-Hello-World" (time from power-on to running the first successful GPU job) for new clusters.
Code Quality: >95% of Pull Requests pass automated linting and require fewer than 2 review cycles before merge.
Stability: Zero "Configuration Drift" incidents in production (e.g., manual changes breaking the cluster) due to strict IaC enforcement.
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