Required Qualifications

• 6+ years of infrastructure engineering experience, with at least 3 years managing GPU compute clusters or HPC environments in production.
• Deep hands-on expertise with NVIDIA GPU infrastructure: driver lifecycle management, CUDA, DCGM, MIG, NVLink topologies, and the NVIDIA GPU Operator for Kubernetes.
• Production-level Kubernetes administration experience on bare-metal: cluster provisioning, upgrades, CNI/CSI configuration, RBAC, and day-2 operations.
• Strong networking fundamentals: BGP, VLAN segmentation, RDMA/RoCE or InfiniBand configuration, load balancing, and firewall policy management.
• Hands-on experience with software-defined storage (Ceph, Rook-Ceph, or MinIO) in AI/HPC workload contexts - performance tuning, capacity planning, and failure recovery.
• Practical MLOps experience: model serving infrastructure (Triton or equivalent), experiment tracking (MLflow or Kubeflow), and GitOps-based model deployment pipelines.
• Working knowledge of NIST SP 800-171 controls and the ability to translate them into concrete infrastructure configurations and audit evidence.
• Proficiency with infrastructure-as-code tooling: Terraform or Ansible for reproducible, auditable infrastructure builds.
• Strong Linux systems administration skills (RHEL/Rocky Linux or Ubuntu) including kernel tuning, storage I/O optimization, and systemd service management.
• Excellent written communication for producing infrastructure runbooks, network diagrams, and compliance documentation in a remote-first environment.

Nice to Have

• Experience with air-gapped or classified network environments and the operational discipline they require (offline package mirrors, USB-controlled media transfers, etc.).
• Familiarity with CMMC Level 2/3 assessment processes and evidence collection.
• Experience with NVIDIA DGX Systems, BasePOD reference architectures, or NVIDIA AI Enterprise software stack.
• Knowledge of distributed training frameworks (PyTorch DDP, DeepSpeed, Megatron-LM) and their infrastructure requirements - useful for supporting AI/ML engineering teammates.
• Experience deploying Kubernetes at the edge: K3s, MicroK8s, or NVIDIA Jetson-based edge clusters.
• Familiarity with observability stacks: Prometheus, Grafana, Loki, OpenTelemetry, and DCGM Exporter for GPU telemetry dashboards.
• US Person status or active security clearance - advantageous for certain client site engagements.
• Background in SCADA, ICS, or OT network environments relevant to critical infrastructure clients.