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Accenture

Advises businesses and runs their technology

AI Infrastructure Operations Engineer

Platform EngineeringConfirmed live in the last 24 hours
Full-timeUnited StatesHybrid

About the Job

Requirements

  • Minimum of 5 years of experience designing, deploying, and managing accelerated-computing infrastructure across on-premises, cloud, and hybrid environments for hyperscaler, neocloud, large enterprise, telecommunications, financial services, manufacturing, and/or retail clients.
  • 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 such as parallel file systems and NVMe-oF.
  • 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, and Run:ai.
  • Minimum 6 months of 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; an associate’s degree is accepted with a minimum of 6 years of 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.
  • Travel as required by business need and client requirements; travel may range from 25% to 60%.

Desired Qualifications

  • Experience building AI infrastructure automation and operations tools and AgenticOps practices that enable secure, automated, governed, and reproducible platform operations.
  • Experience developing reusable infrastructure code leveraging Python, platform services, and automation workflows using REST APIs, OpenAPI, JSON/YAML schemas, webhooks, and event-driven integrations.
  • Experience operating large-scale GPU clusters, including capacity management, reliability engineering, change management, and performance validation for AI training, inference, high-performance computing, and enterprise compute workloads.
  • Experience using NVIDIA platform tools and libraries, including Base Command Manager (BCM), NGC, NCCL, CUDA-X, NVAIE, Dynamo, and benchmarking tools, to deploy, tune, profile, and validate cluster performance for training and inference workloads.
  • Experience managing deployments of 1,000+ GPU clusters with infrastructure services enabled for AI training, inference, high-performance, and enterprise compute environments.
  • Design and build experience running large language models across AI cloud platforms from CoreWeave, Nebius, and other specialty providers.
  • Knowledge of model deployments, tuning, and performance troubleshooting on AI infrastructure.
  • Industry certifications in accelerated-computing infrastructure, public cloud providers, infrastructure automation, networking, or security.

About the Company

Accenture

Advises businesses and runs their technology

Accenture advises large businesses and public agencies on strategy, builds their technology systems and runs ongoing operations. Its teams connect business planning with implementation, covering areas such as cloud migration, cybersecurity and customer experience. Consultants work with clients to redesign processes, while engineers integrate software from technology partners and maintain the resulting systems for multinational organizations. Clients pay for project work or continuing services, including application support and business processes such as finance and customer service. Accenture was founded in 1989 and has its headquarters in Dublin, Ireland.

Company size
10,001+
Company stage
IPO
Headquarters
Dublin, Ireland
Founded
1989

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