AI Infrastructure Technical Systems Engineer

The application window is expected to close on: 11/22/2026

The Cisco IT AI Infrastructure team delivers high-performance compute (HPC) and accelerated AI infrastructure as a service across Cisco's global enterprise footprint. We provide foundational supercomputing platforms that empower engineering, research, security, and product teams. Our organization architects, builds, and operates the enterprise-grade supercomputing infrastructure that powers Cisco's foundational models, distributed AI training, inference engines, and machine learning platforms.

As an AI Infrastructure Technical System Engineer and Technical Leader, you will serve as the principal systems architect and technical authority for our enterprise AI platforms. You will establish architectural direction, spearhead complex infrastructure initiatives, mentor engineering talent, and bridge accelerated hardware capabilities with high-performance software execution.

You will collaborate with a global multi-disciplinary team, interfacing regularly with internal AI Researchers, Cisco Hardware & Network Architecture Teams, and Industry Ecosystem Partners (NVIDIA, AMD, Red Hat) to engineer and operate a cutting-edge, large-scale AI technology stack.

Architecture, Strategy & Capacity Engineering

• Lead the architectural design, hardware qualification, capacity planning, and maintenance of large-scale bare-metal GPU clusters (NVIDIA/AMD).
• Architect resilient, multi-tenant compute and control planes across bare-metal Kubernetes, enterprise container platforms (Red Hat Open Shift), and HPC cluster managers (NVIDIA BCM).
• Collaborate with network engineering to design, benchmark, and optimize high-throughput, low-latency backend fabrics (RDMA, RoCEv2, 400G/800G).

Infrastructure as Code (IaC), Git Ops & SRE Automation

• Champion Site Reliability Engineering to eliminate operational toil, drive autonomous self-healing, and uphold stringent Service Level Objectives (SLOs).
• Architect IaC and Git Ops deployment pipelines using Ansible, Terraform, Python, Go, and modern CI/CD tooling.
• Oversee automated workflows for firmware baselines, BIOS/BMC configuration, kernel driver updates, platform upgrades, and certificate rotations.

AI Workload Orchestration & Systems Performance

• Optimize distributed workload scheduling, fair-share queueing, and GPU quota governance across frameworks (Slurm, Kueue, Volcano, Ray).
• Profile low-level systems performance, diagnosing device interconnect bottlenecks, bandwidth utilization, memory hierarchies, and compute efficiency.
• Establish automated Day-2 diagnostics and recovery procedures for GPU degradation, driver anomalies, and hardware faults.

Observability, Governance & Technical Leadership

• Architect end-to-end observability standards using Open Telemetry (OTel), Prometheus, and Splunk to deliver role-specific visualization dashboards and anomaly detectors.
• Serve as the senior escalation authority for high-impact incidents, leading root-cause analyses (RCAs) and aligning operations with ITIL frameworks.
• Author architecture blueprints and standard operating procedures (SOPs); mentor and coach mid-level and senior engineers to foster a culture of innovation.

• Bachelor's degree in Computer Science, Engineering, IT, or related discipline with 5 plusof progressive experience in enterprise systems engineering, HPC, AI infrastructure, or SRE.
• Proficiency in Python, Go, or Bash, with extensive experience building declarative automation (Ansible, Terraform, CI/CD, Git Ops).
• Advanced expertise designing and administering production Kubernetes, Red Hat Open Shift, and HPC workload managers (Slurm, Kueue, Volcano, Ray).
• Strong hands-on understanding of AI backend network fabrics, RDMA technologies (RoCEv2), and high-speed multi-hundred-gigabit network topologies.
• In-depth knowledge of enterprise Linux (RHEL/Ubuntu), kernel module lifecycle, memory subsystems, GPU device drivers, and out-of-band management (Redfish, IPMI).

• Master's degree in Computer Science, Computer Engineering, or a related technical discipline (or an additional 2+ years of equivalent specialized engineering experience).
• 3+ years of experience managing enterprise server platforms at scale (e.g., environments with 500+ nodes), including automated hardware provisioning and centralized infrastructure management solutions.
• 2+ years of hands-on experience deploying, troubleshooting, and optimizing heterogeneous GPU software…