Consultant – Operations Oversight & Performance, AI Infrastructure
Job Description & How to Apply Below
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.
- 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 Git Ops 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; elevate 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.
- 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.
- 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
- Open Shift Container Platform 4.6 - Red Hat Certified Specialist in Open Shift Administration
- Any service management or performance management credentials - advantageous
- Relevant Linux or systems certifications (RHCSA or equivalent) - advantageous
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