Cisco
AI Infrastructure Technical Systems Engineer
$140K–$247K
RTP, North Carolina, US +1 · Hybrid · Full-time · Posted Oct 23, 2026
- Location
- RTP, North Carolina, US
San Jose, California, US - Experience
- 5+ years of progressive experience in enterprise systems engineering, HPC, AI infrastructure, or SRE.
- Education
- Bachelor’s degree in Computer Science, Engineering, IT, or related discipline.
About the job
Architects and operates large-scale GPU and HPC infrastructure for Cisco’s enterprise AI platforms, including distributed training, inference, and machine learning workloads.
What you'll do
- Lead GPU cluster architecture, hardware qualification, capacity planning, and backend network optimization.
- Build IaC and GitOps automation; improve reliability, workload scheduling, performance, observability, and recovery.
- Lead incident response and root-cause analysis, author technical standards, and mentor engineers.
What they ask for
- Bachelor’s degree in Computer Science, Engineering, IT, or a related discipline.
- Proficiency in Python, Go, or Bash and extensive experience with declarative automation, including Ansible, Terraform, CI/CD, and GitOps.
- Advanced expertise with production Kubernetes, Red Hat OpenShift, and HPC workload managers such as Slurm, Kueue, Volcano, or Ray.
- Strong knowledge of RDMA/RoCEv2 networks and enterprise Linux, kernel modules, GPU drivers, memory systems, and Redfish or IPMI.
Nice to have
- Master’s degree or an additional 2+ years of equivalent specialized engineering experience; advanced industry certification or open-source infrastructure contributions.
- Experience at scale with enterprise servers, GPU software stacks, secrets management, or identity federation.
Pay
$140,300.00 to $178,100.00 (base salary); $168,300.00 - $247,000.00 (base salary); $150,200.00 - $218,400.00 (base salary)
Tools
NVIDIA, AMD, Kubernetes, Red Hat OpenShift, Slurm, Kueue, Volcano, Ray
AI focus
Supports enterprise AI infrastructure for foundational models, distributed AI training, inference engines, and machine learning platforms; optimizes GPU workloads and systems performance.
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