Consultant – Operations Oversight & Performance, AI Infrastructure

Job Description The Consultant – Operations Oversight & Performance, AI Infrastructure is responsible for Oversee OEM ongoing operational delivery after go-live. Ensure the GPU infrastructure AI Runtime Layer - Kubernetes, GPU scheduling, container runtime, workloads, and autoscaling are operated to agreed performance standards and drive continuous improvement through structured oversight. Responsibilities Define, agree, and monitor operational KPIs and SLAs with 3rd party (availability, incident response, resolution times, change success rate, capacity utilization, etc.). Escort Operation and hardening of Kubernetes clusters with GPU Operator, device plugins, DRA, MIG/time-slicing, and advanced schedulers (Kueue or equivalents). Review GPU-aware scheduling, fair-share/quotas, priority classes, preemption, and multi-tenancy isolation. Supervise manages container runtime, AI workloads (training/inference), and autoscaling. Review integrate observability (Prometheus/Grafana + DCGM), logging, and GitOps for the runtime layer. Support multi-cluster federation and cluster lifecycle (in partnership with Rafay layer). Conduct regular operational reviews of Advanced NOC performance, incident trends, problem management, and change records. Drive service improvement plans and hold 3rd party accountable for operational performance. Review capacity, lifecycle, and maintenance plans; escalate risks to the Program Delivery Lead. Ensure operational processes support future Platform foundation requirements (observability, identity, scheduling readiness). Coordinate planned works, maintenance windows, and operational communications with internal stakeholders. Maintain the operational risk register and support audit/compliance evidence collection. Qualifications Required Experience & Background: 10 years in GPU and HPC infrastructure operations, service management, or managed-service oversight roles. 7+ years Kubernetes (CKA preferred); deep experience running GPU workloads at scale. Strong knowledge of CNCF ecosystem, operators, CRDs, and GPU-specific features (NVIDIA GPU Operator, DRA). Excellent understanding of GPU or high-performance compute operations, observability, and incident/change management. Strong analytical, reporting, and stakeholder management skills. Preferred Certifications NCP-AII – NVIDIA-Certified Professional: AI Infrastructure NCP-AIO – NVIDIA-Certified Professional: AI Operations NCP-AIN – NVIDIA-Certified Professional: AI Networking Red Hat Certified Specialist in Containers and Kubernetes – OpenShift Container Platform 4.6 Red Hat Certified Specialist in OpenShift Administration Any service management or performance management credentials – advantageous Relevant Linux or systems certifications (RHCSA or equivalent) – advantageous Show more Show less

10 years in GPU and HPC infrastructure operations, service management, or managed-service oversight roles. 7+ years Kubernetes (CKA preferred); deep experience running GPU workloads at scale. Strong knowledge of CNCF ecosystem, operators, CRDs, and GPU-specific features (NVIDIA GPU Operator, DRA). Excellent understanding of GPU or high-performance compute operations, observability, and incident/change management. Strong analytical, reporting, and stakeholder management skills.

Define, agree, and monitor operational KPIs and SLAs with 3rd party (availability, incident response, resolution times, change success rate, capacity utilization, etc.). Escort Operation and hardening of Kubernetes clusters with GPU Operator, device plugins, DRA, MIG/time-slicing, and advanced schedulers (Kueue or equivalents). Review GPU-aware scheduling, fair-share/quotas, priority classes, preemption, and multi-tenancy isolation. Supervise manages container runtime, AI workloads (training/inference), and autoscaling. Review integrate observability (Prometheus/Grafana + DCGM), logging, and GitOps for the runtime layer. Support multi-cluster federation and cluster lifecycle (in partnership with Rafay layer). Conduct regular operational reviews of Advanced NOC performance, incident trends, problem management, and change records. Drive service improvement plans and hold 3rd party accountable for operational performance. Review capacity, lifecycle, and maintenance plans; escalate risks to the Program Delivery Lead. Ensure operational processes support future Platform foundation requirements (observability, identity, scheduling readiness). Coordinate planned works, maintenance windows, and operational communications with internal stakeholders. Maintain the operational risk register and support audit/compliance evidence collection.