AI Infra Engineer: GPU Clusters, Automation & HPC

Accenture; Detroit (MI); On-site; USD 110,000 - 210,000; Full time

Benefits offered by this job

Medical benefits

Dental benefits

401(k) plan

Job summary

Accenture is seeking an experienced Senior AI Infrastructure Engineer to design and operate accelerated‑computing infrastructure for AI training and inference across on‑premises, cloud, and hybrid environments. You will deploy GPU clusters, manage scheduling with Kubernetes and Slurm, and build automation tools to ensure performance, resiliency, and scalability.

The role requires 5+ years on accelerated infrastructure, with hands‑on experience in GPUs, DPUs, and CPUs, plus proficiency in

Qualifications

• Minimum of 5+ years of experience designing, deploying, and managing accelerated-computing infrastructure across on-premises, cloud, and hybrid environments.
• Minimum of 5+ years of hands‑on experience with accelerated‑computing platforms, including GPUs, DPUs, and CPUs, high‑bandwidth network fabrics, and AI based storage architectures.
• Minimum of 5+ years of experience with cluster management, workload scheduling, orchestration, observability, and infrastructure automation, including building operational tools and automation workflows with platforms such as Kubernetes, Slurm, Run:ai
• Minimum 6 months hands‑on experience with Claude Code, AI automation tools, Terraform, Ansible, Python, and Bash scripting.
• Bachelor's degree or equivalent (minimum 12 years) work experience.

Responsibilities

• Design and implement accelerated-computing infrastructure solutions aligned to system architecture, deployment roadmaps, performance, scalability, resiliency, and governance requirements.
• Deploy, configure, and operate GPU-based clusters across bare-metal and containerized environments, using workload schedulers and Kubernetes orchestration to support AI training, inference, and high-performance compute workloads.
• Integrate infrastructure platforms with enterprise systems, data platforms, security frameworks, service-management processes, and governance controls.
• Design, build, and maintain reusable tools, scripts, self-service capabilities, and automation workflows for infrastructure operations, including provisioning, configuration management, validation, capacity planning, monitoring, incident management, reporting, and recurring remediation.
• Establish repeatable operational processes for cluster provisioning, configuration management, patching, capacity planning, monitoring, incident response, and lifecycle management.
• Perform and automate GPU, compute, storage, and network benchmarking and validation; diagnose performance issues across multi-node AI training, inference, and distributed compute workloads.
• Develop and maintain architecture diagrams, configuration baselines, operational runbooks, and support documentation.
• Provide technical guidance, troubleshooting, and optimization for GPU clusters supporting AI training, inference, high-performance computing, and multi-node simulation workloads, with emphasis on availability, resiliency, scalability, energy efficiency, and cost management.

Skills

GPU clusters

Cluster management

Workload orchestration

Kubernetes

Slurm

Run:ai

Claude Code

Python

Bash scripting

Education

Bachelor's degree or equivalent (12 years)

Tools

Terraform

Ansible

Claude Code

Job description

Accenture is seeking an experienced Senior AI Infrastructure Engineer to design and operate accelerated‑computing infrastructure for AI training and inference across on‑premises, cloud, and hybrid environments. You will deploy GPU clusters, manage scheduling with Kubernetes and Slurm, and build automation tools to ensure performance, resiliency, and scalability.

The role requires 5+ years on accelerated infrastructure, with hands‑on experience in GPUs, DPUs, and CPUs, plus proficiency in