Accenture

AI & HPC Infrastructure Engineer

Accenture$94K — $266K *
Enterprise Technology
5 - 7 years of experience
Job Overview by Ladders

Qualifications

  • 5+ years of experience in AI infrastructure and accelerated computing environments across various sectors.
  • Hands-on experience with GPUs, DPUs, LPUs, CPUs, and high-speed interconnects.
  • Proficient in Kubernetes, Slurm, Run:ai, AWS, Azure, GCP, and automation tools.
  • Bachelor's degree or 12 years equivalent work experience, or 6 years with an Associate's degree.

Responsibilities

  • Design and implement AI infrastructure and computing solutions according to industry needs.
  • Deploy and manage XPU-based clusters in containerized environments with workload schedulers.
  • Integrate AI platforms with existing IT systems and security frameworks.
  • Build and maintain systems for monitoring and optimizing AI infrastructure performance.
  • Architect and deploy services using NVIDIA tools for AI workloads.
  • Maintain documentation including architecture diagrams and operational runbooks.
  • Provide technical guidance on AI workload management and optimization.

Benefits

  • Medical, dental, and vision insurance coverage.
  • 401(k) plan with company contributions.
  • Bonus opportunities based on performance.
  • Paid holidays and time off for rest and relaxation.
Full Job Description

Key Responsibilities:

  • Design and implement AI infrastructure and accelerated computing solutions, aligning system architecture and deployment roadmaps to industry-specific performance, scalability, resiliency, and governance needs

  • Deploy, configure, and manage XPU-based clusters (GPU, DPU, LPU, CPU) across bare-metal and containerized environments using workload schedulers (Slurm, Run:ai), Kubernetes orchestration, and container platforms to deliver scalable AI infrastructure services including Bare-Metal-aaS, GPUaaS, AIaaS, Token-aaS, model serving, and agentic AI frameworks

  • Integrate AI infrastructure platforms with existing IT systems, data pipelines, security frameworks, model-serving endpoints, and enterprise governance controls

  • Design and implement agentic AI infrastructure by integrating platform services, model endpoints, tool and function calling, retrieval patterns, and workflow orchestration with observability, identity, and policy controls through secure, deterministic APIs to support governed enterprise use cases

  • Build and integrate MCP servers, tools, connectors, and adapters that allows agents to monitor, troubleshoot, and tune infrastructure to ensure high availability, low-latency networking, and workload resiliency

  • Architect and deploy with NVIDIA platform tools including Base Command Manager (BCM), NGC, NCCL, NVLink, and CUDA along with LLM inference engines (TensorRT-LLM), production serving frameworks (vLLM, SGLang), inference orchestration (Triton Inference Server, NVIDIA Dynamo, llm-d), and GPU benchmarking and validation tools (MLPerf, NCCL tests, fio, iperf) to deploy, tune, profile, and validate AI cluster performance across compute and networking layers including multi-node training and inference workloads

  • Develop and maintain documentation including architecture diagrams, configuration baselines, and operational runbooks

  • Provide technical guidance, troubleshooting, and optimization across AI workloads including large-scale training, inference, multi-node simulations, and agentic pipelines while leveraging digital twins to validate infrastructure and drive performance, scalability, energy efficiency, and token cost optimization

Travel may be required for this role.  The amount of travel will vary from 25% to 100% depending on business need and client requirements.

Required Skills and Qualifications:

  • Minimum of 5+ years of experience designing, deploying, and managing AI infrastructure and accelerated computing environments across on-premises, cloud, and hybrid environments for hyperscaler, neocloud, large enterprise, Telco/Mobile, Financial Services, Life Sciences, Manufacturing, and/or Retail clients.

  • Minimum of 5+ years of hands-on experience with accelerated computing platforms, including GPUs, DPUs, LPUs, CPUs, high-speed interconnects such as InfiniBand or Ethernet, data center networking such as SONiC, and AI storage architectures including NVMe, NVMe-oF, parallel file systems, VAST, Weka, or DDN.

  • Minimum of 5+ years of experience with cluster management, workload scheduling, orchestration, observability, and infrastructure automation using platforms and tools such as Kubernetes, Slurm, Run:ai, AWS, Azure, GCP, VMware, Nutanix, Python, Terraform, and Ansible.

  • Bachelor's degree or equivalent (minimum 12 years) work experience. If Associate's Degree, must have minimum 6 years work experience.

Preferred Skills and Qualifications:

  • 2+ years of experience implementing MLOps, LLMOps, agentic AI, and DevSecOps frameworks to enable secure, automated, governed, and reproducible AI workflows.

  • 2+ years of experience developing APIs, integration services, automation workflows, or platform services using Python and modern API patterns such as REST, OpenAPI, JSON/YAML schemas, webhooks, and event-driven integrations.

  • Experience designing and implementing agentic AI infrastructure, including LLM inference, tool/function calling, retrieval-augmented generation (RAG), agent orchestration, secure API integration, policy-based governance, and deterministic platform APIs.

  • Experience building and integrating MCP servers, tools, connectors, and adapters that allow agents to monitor, troubleshoot, and tune infrastructure for high availability, low-latency networking, workload resiliency, and intelligent observability.

  • Experience using NVIDIA platform tools including Base Command Manager (BCM), NGC, NCCL, NVLink, CUDA, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, llm-d, vLLM, SGLang, MLPerf, NCCL tests, fio, and iperf to deploy, tune, profile, and validate AI cluster performance.

  • Experience managing the deployment of 1,000+ GPU clusters for AI, HPC, and agentic AI workloads with infrastructure services enabled.

  • Design and build experience in AI Cloud platforms from CoreWeave, Nebius, and other specialty cloud providers.

  • Knowledge of machine learning and AI frameworks such as TensorFlow, PyTorch, JAX, Jupyter notebooks, and Google Colab environments.

  • Industry certifications in NVIDIA infrastructure, public cloud providers, data science, infrastructure automation, networking, or security are a plus.


Compensation at Accenture varies depending on a wide array of factors, which may include but are not limited to the specific office location, role, skill set, and level of experience. As required by local law, Accenture provides a reasonable range of compensation for roles that may be hired as set forth below.
We anticipate this job posting will be posted until 10/15/2026.

Accenture offers a market competitive suite of benefits including medical, dental, vision, life, and long-term disability coverage, a 401(k) plan, bonus opportunities, paid holidays, and paid time off. See more information on our benefits here:


Role Location Annual Salary Range
California $94,400 to $266,300
Cleveland $87,400 to $213,000
Colorado $94,400 to $230,000
District of Columbia $100,500 to $245,000
Illinois $87,400 to $230,000
Maine $80,400 to $196,000
Maryland $94,400 to $230,000
Massachusetts $94,400 to $245,000
Minnesota $94,400 to $230,000
New York $87,400 to $266,300
New Jersey $100,500 to $266,300
Virginia $87,400 to $245,000
Washington $100,500 to $245,000

About Accenture

Accenture plc is a multinational professional services company that provides services in strategy, consulting, digital, technology, and operations. The company has more than 537,000 employees serving clients in more than 120 countries. Accenture operates across five business segments: Communications, Media & Technology; Financial Services; Health & Public Service; Products; and Resources. The company is headquartered in Dublin, Ireland, and has offices worldwide.
Learn more about Accenture
Size
624,000 employees
Market Cap
$173.8 billion
Industry
Net Income
$5.2 billion
Founded
1989
5 Year Trend
+11.2%
Revenue
$44.7 billion
NASDAQ

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