Infrastructure Engineer – AI/ML & GPU Platforms

About the role

Lead enterprise infrastructure initiatives supporting Generative AI, Predictive AI, and large-scale GPU computing platforms.

Tasks

• Design, deploy, and operate AI/ML infrastructure platforms from development through production.
• Build and manage OpenShift-based container platforms optimized for GPU workloads.
• Support scalable GPU SuperPod architectures and large multi-node GPU clusters.
• Manage monitoring, alerting, and observability using Grafana, Splunk, and telemetry tools.
• Define SLIs/SLOs and proactively identify performance, capacity, and resiliency risks.
• Develop AI-driven automation for self-healing, scaling, diagnostics, and incident remediation.
• Apply AIOps techniques to improve reliability and reduce alert fatigue.
• Lead production incident analysis, troubleshooting, and root-cause prevention.
• Support high availability, business continuity, and disaster recovery initiatives.
• Collaborate with distributed teams and mentor fellow engineers.

Requirements

• 5+ years of Infrastructure Engineering experience.
• 5+ years of complex end-to-end architecture troubleshooting, including CI/CD pipelines.
• 5+ years of Linux systems experience.
• 4+ years supporting AI/ML platforms.
• 4+ years of Kubernetes/container platform experience, including production support.
• Local Candidates Only with local DL
• USC/GC Preferred
• Generative AI and Predictive AI platforms
• Kubernetes and OpenShift in GPU-enabled environments
• GPU scheduling, quota management, and performance tuning
• GPU SuperPod architecture and multi-node clusters

Schedule

• Onsite
• C2C Preferred

Location

• Charlotte, NC
• Chandler, AZ
• Dallas, TX
• Iselin, NJ