AI Infra Operations Engineer: GPU Clusters & Automation

Accenture; Oklahoma City (OK); On-site; USD 120,000 - 210,000; Full time

Job summary

Accenture seeks a Senior Infrastructure Engineer to design, deploy, and operate accelerated-computing infrastructure for AI training and inference across on-premises, cloud, and hybrid environments. You will lead GPU cluster management, workflow automation, and integration with security, data platforms, and governance, with travel as needed to client sites.

Collaboration across teams is essential for scalable, cost-aware operations.

Qualifications

• Minimum 5+ years designing, deploying, and managing accelerated-computing infrastructure
• Experience with GPUs, DPUs, high-bandwidth networks, AI storage architectures
• Experience with cluster management, workload scheduling, orchestration, and automation
• 6 months hands-on with Claude Code, AI automation tools, Terraform, Ansible, Python, Bash
• Bachelor's degree or equivalent work experience (12 years)

Responsibilities

• Design accelerated-computing infrastructure solutions aligned to architecture, roadmaps, and governance
• Deploy and operate GPU-based clusters across bare-metal and containerized environments
• Integrate infrastructure platforms with enterprise data, security, and service-management processes
• Develop reusable tools and automation workflows for provisioning, config, monitoring, and remediation
• Establish repeatable processes for provisioning, patching, capacity planning, and incident response
• Benchmark and diagnose performance across multi-node AI training and inference workloads
• Create architecture diagrams, baseline configurations, and runbooks
• Provide guidance and optimization for GPU clusters supporting AI workloads

Skills

GPU clusters management

Kubernetes orchestration

Automation tooling

Python scripting

Education

Bachelor's degree or equivalent

Tools

Terraform

Ansible

Run:AI

NVIDIA tools

Job description

Accenture seeks a Senior Infrastructure Engineer to design, deploy, and operate accelerated-computing infrastructure for AI training and inference across on-premises, cloud, and hybrid environments. You will lead GPU cluster management, workflow automation, and integration with security, data platforms, and governance, with travel as needed to client sites.

Collaboration across teams is essential for scalable, cost-aware operations.