AI Infrastructure Engineer: HPC, GPUs & Agentic AI

Accenture; Walnut Creek (CA); On-site; USD 94,000 - 266,000; Full time

Job summary

Accenture is seeking a senior AI infrastructure engineer in California to design, deploy, and operate GPU‑powered AI clusters across on‑prem, cloud, and hybrid environments. You will implement scalable architectures, integrate with existing IT, and drive governance and observability for enterprise AI workloads.

The role requires 5+ years in AI infrastructure, strong knowledge of GPUs and interconnects, and hands‑on experience with Kubernetes, Slurm, Run:ai, and automation tools.

Qualifications

• Bachelor's degree or equivalent (minimum 12 years) work experience, or 6 years with an Associate's degree.
• 5+ years designing, deploying, and managing AI infrastructure across on‑prem, cloud, and hybrid environments.
• Hands-on experience with GPUs, DPUs, LPUs, high‑speed interconnects, and AI storage.
• Experience with Kubernetes, Slurm, Run:ai, and modern automation tools.

Responsibilities

• Design and implement AI infrastructure and accelerated computing solutions with scalable, governed architectures.
• Deploy and manage XPU-based clusters across bare‑metal and containerized environments.
• Integrate AI platforms with IT systems, data pipelines, and governance controls.
• Develop agentic AI infrastructure including model endpoints and tool calling.
• Provide documentation and runbooks; guide optimization across AI workloads.

Skills

AI infrastructure design

Kubernetes

Slurm

Run:ai

Python

NVIDIA GPUs

Cloud platforms

Education

Bachelor's degree or equivalent (minimum 12 years) work experience

Tools

AWS

Azure

GCP

Terraform

Ansible

Job description

Accenture is seeking a senior AI infrastructure engineer in California to design, deploy, and operate GPU‑powered AI clusters across on‑prem, cloud, and hybrid environments. You will implement scalable architectures, integrate with existing IT, and drive governance and observability for enterprise AI workloads.

The role requires 5+ years in AI infrastructure, strong knowledge of GPUs and interconnects, and hands‑on experience with Kubernetes, Slurm, Run:ai, and automation tools.