Infra Transformation Manager, Senior Level — AI & HPC Infrastructure Engineer

Job title: Infra Transformation Manager, Senior Level — AI & HPC Infrastructure Engineer

About the Role The Global AI Infrastructure team enables infrastructure modernization for AI platforms, accelerated computing, and high-performance workloads. We design, build, and operate AI infrastructure across cloud, on-premises, and hybrid environments to support scalable, governance-driven AI solutions.

What You'll Do

• Design and implement AI infrastructure and accelerated computing solutions, aligning system architecture and deployment roadmaps to performance, scalability, resiliency, and governance needs.
• Deploy, configure, and manage XPU-based clusters (GPU, DPU, LPU, CPU) across bare-metal and containerized environments using Slurm, Run:ai, Kubernetes, and container platforms to deliver scalable AI 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 allow 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 BCM, NGC, NCCL, NVLink, 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; amount of travel varies from 25% to 100% depending on business need and client requirements.

What We're Looking For

• 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.

Nice to Have

• 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 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.