This role pays more than
64%
of similar roles. Most pay
$144,350–$203,141
— the shaded band above.
At the midpoint, this role pays about
$180k
versus about
$174k
for comparable roles.
Based on 240 similar postings.
Employer
About Accenture
Accenture is a leading global professional services company specializing in IT, strategy, consulting, and operations, with a strong focus on digital transformation, cloud computing, and artificial intelligence.
Accenture currently has
192 open roles
on FindRole.
Listed pay typically runs
$94,400–$266,300
across 165 roles with salary data.
As an AI Infrastructure Operations Engineer on the Global AI Infrastructure team, you will design, build, and operate large-scale GPU and accelerated-computing infrastructure to support demanding AI training, inference, and high-performance compute workloads across cloud, on-premises, and hybrid environments. You will develop reusable operational tools, automation workflows, and platform capabilities to streamline tasks like provisioning, configuration management, and capacity planning. Your daily work involves managing GPU-based clusters in bare-metal and containerized environments using Kubernetes and Slurm. To succeed, you must possess expertise in high-bandwidth network fabrics, NVMe-oF storage architectures, and NVIDIA tools like NCCL and CUDA-X. You will utilize Python, Bash, Terraform, Ansible, and Claude Code to automate infrastructure operations. This role solves the challenge of providing resilient, scalable, and energy-efficient compute environments for complex multi-node simulation and distributed training workloads.
Design and implement accelerated-computing infrastructure solutions for AI training, inference, and high-performance workloads.
Deploy and manage GPU-based clusters across bare-metal and containerized environments using Kubernetes and other schedulers.
Build automated workflows and reusable tools for provisioning, configuration management, monitoring, and incident response.
Integrate infrastructure platforms with enterprise systems, security frameworks, and service-management processes.
Perform and automate benchmarking and validation for GPU, compute, storage, and network components.
Develop technical documentation including architecture diagrams, configuration baselines, and operational runbooks.
Provide expert troubleshooting and optimization for multi-node AI training and distributed compute workloads.
What we're looking for
Must have 5+ years of experience designing, deploying, and managing accelerated-computing infrastructure in on-premises, cloud, and hybrid environments.
Must have 5+ years of hands-on experience with GPUs, DPUs, CPUs, high-bandwidth network fabrics, and AI-based storage architectures.
Must have 5+ years of experience with cluster management, workload scheduling, orchestration, observability, and infrastructure automation.
Must have at least 6 months of experience with Claude Code, AI automation tools, Terraform, Ansible, Python, and Bash scripting.
Must possess a Bachelor's degree or equivalent (minimum 12 years of work experience).
Candidates with an Associate’s Degree must have at least 6 years of work experience.
Ability to travel between 25% and 60% as required by business needs and client requirements.
C
C++
Python
Java
Embedded Systems
RTOS
Linux
Android
Windows
DSP
IPC
Memory Management
Concurrency
Machine Learning
Computer Vision
System Optimization