AI Infrastructure Ops Engineer: Scalable GPU & Hybrid Cloud

Accenture; Hartford (CT); On-site; USD 120,000 - 240,000; Full time

Job summary

Accenture seeks a seasoned infrastructure engineer to design, deploy, and operate accelerated-computing infrastructure for AI workloads across on-premises, cloud, and hybrid environments. You will build GPU clusters, optimize performance, and implement automation to scale across multi-node deployments.

Candidates should have hands-on experience with Kubernetes, workload schedulers, and infrastructure-as-code tools, plus strong scripting skills and a BA/BS.

Qualifications

• 5+ years designing, deploying, and managing accelerated-computing infra across on-prem, cloud, and hybrid environments.
• 5+ years hands-on experience with GPUs, DPUs, high-bandwidth networks, and AI storage architectures.
• 5+ years in cluster management, workload scheduling, orchestration, and infra automation (Kubernetes, Slurm, Run:ai).
• 6 months hands-on with Claude Code, AI automation tools, Terraform, Ansible, Python, and Bash.

Responsibilities

• Design and implement accelerated-computing infrastructure solutions aligned to architecture, roadmaps, performance, and governance.
• Deploy, configure, and operate GPU-based clusters across bare-metal and containerized environments with Kubernetes orchestration.
• Integrate infrastructure platforms with enterprise systems, data platforms, security frameworks, and governance controls.
• Develop and maintain reusable tools, scripts, and automation workflows for provisioning, configuration, monitoring, and remediation.
• Establish repeatable processes for provisioning, configuration management, patching, and lifecycle management.

Skills

Kubernetes

Slurm

Run:ai

Python

Bash

GPU clusters

Networking

Storage

Education

Bachelor's degree or equivalent

Tools

NVIDIA BCM

NGC

NCCL

CUDA-X

Terraform

Ansible

Job description

Accenture seeks a seasoned infrastructure engineer to design, deploy, and operate accelerated-computing infrastructure for AI workloads across on-premises, cloud, and hybrid environments. You will build GPU clusters, optimize performance, and implement automation to scale across multi-node deployments.

Candidates should have hands-on experience with Kubernetes, workload schedulers, and infrastructure-as-code tools, plus strong scripting skills and a BA/BS.