AI & HPC Infrastructure Engineer

We Are:

The Global AI Infrastructure team is at the center of enabling infrastructure reinvention for the next era of digital solutions powered by AI, accelerated computing, and high-performance workloads. We bring together deep technical expertise across cloud, on-premises, and hybrid environments to design, build, and operate advanced infrastructure that powers AI platforms, GPU-accelerated workloads, large-scale models, simulations, and emerging agentic AI solutions  solutions enable some of our most strategic and mission-critical clients to unlock new levels of performance, efficiency, governance, and innovation.

Our remit spans the full lifecycle-from strategy and architecture through implementation and operations-driving modernization across the entire infrastructure stack. We collaborate across the ecosystem to harness emerging technologies, fuel growth, and transform industries. In this rapidly growing market, our team is leading the way in shaping how enterprises leverage AI infrastructure to drive breakthrough innovation and reimagine what is possible.

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 Infini Band 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 Dev Sec Ops  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,…