Note: The job is a remote job and is open to candidates in USA. Catapult Federal Services is a professional services firm that provides technology guidance and solutions through experienced engineering teams. The AI Infrastructure & Kubernetes Platform Engineer will deploy and operate NVIDIA DGX AI clusters, build GPU-accelerated Kubernetes platforms, administer InfiniBand and BlueField DPU networking, and secure AI/ML infrastructure. The role also focuses on monitoring, performance optimization, CI/CD, GitOps, and operational reliability.
Responsibilities
- Deploy and manage NVIDIA DGX BasePODs and SuperPODs for high-performance AI workloads
- Oversee DGX system lifecycle operations, including provisioning, monitoring, firmware upgrades, and capacity planning
- Operate Base Command Manager to manage GPU clusters, schedule workloads, and integrate with MLOps tools
- Perform DGX node health validation, NCCL interconnect testing, and NVLink topology verification after deployments or hardware changes
- Architect secure, scalable Kubernetes clusters optimized for GPU-accelerated workloads using the NVIDIA GPU Operator
- Apply CKA/CKAD/CKS expertise to develop, deploy, and secure AI applications on Kubernetes
- Implement CI/CD pipelines and GitOps methodologies for deploying and managing ML workflows
- Administer InfiniBand networks and BlueField DPUs using Unified Fabric Manager (UFM)
- Enable NVLink/NVSwitch performance across GPU nodes and tune fabric configurations for minimal latency and maximum throughput
- Use BlueField to offload storage, firewalling, and telemetry, strengthening AI workload security and performance
- Apply CKS best practices to secure containerized AI environments
- Configure runtime security, secrets management, network segmentation, and auditing across DPU-enhanced Kubernetes deployments
- Support zero-trust initiatives by enforcing workload identity, RBAC policies, and supply-chain integrity across AI container images and model artifacts
- Monitor GPU, CPU, and I/O performance using NVIDIA DCGM, Prometheus, Grafana, and Base Command APIs
- Tune system performance and model-training pipelines for cost-efficiency and throughput
- Build and maintain operational runbooks, incident-response playbooks, and SLA dashboards covering GPU utilization, thermal thresholds, and fabric health
Skills
- Certified Kubernetes Administrator (CKA)
- Certified Kubernetes Application Developer (CKAD)
- Certified Kubernetes Security Specialist (CKS)
- NVIDIA Certified Associate: AI Infrastructure & Operations (NCA-AIIO)
- NVIDIA Certified Professional: AI Infrastructure (NCP-AII)
- NVIDIA Certified Professional: AI Operations (NCP-AIO)
- NVIDIA Certified Professional: AI Networking (NCP-AIN)
- DGX System, BasePOD, and SuperPOD administration
- BlueField DPU configuration and operations
- InfiniBand fabric and UFM management
- Base Command Manager for workload orchestration
- Kubernetes, Helm, and the NVIDIA GPU Operator
- DevOps tooling: Ansible, Terraform, GitOps, CI/CD pipelines
- Programming/scripting: Python, YAML, Bash
- Bachelor's degree in Computer Science, Engineering, or a related field — or equivalent hands-on experience
- Kubeflow and broader MLOps pipeline experience
- Parallel/HPC storage: NFS, BeeGFS, Lustre
- Advanced networking: RoCE, RDMA, gRPC, and DPU offload tuning
Benefits
- Medical/health coverage
- Paid time off
- 401(k) retirement savings
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