AI Infrastructure Specialist

5 days ago


Tirupati, Andhra Pradesh, India beBeeExpertise Full time ₹ 1,80,00,000 - ₹ 2,00,00,000

Job Title: AI Infrastructure Engineer

Description:

We are seeking a highly skilled engineer to design and build platforms for AI workloads in enterprise environments.

  • Kubernetes Expertise:
    • A deep understanding of container orchestration platforms, particularly Red Hat OpenShift and CNCF Kubernetes.
    • Proficiency in deploying and supporting GPU-accelerated workloads and tools like NVIDIA GPU Operator and DCGM.
    • The ability to integrate Kubernetes with AI/ML workloads and GPU infrastructure in hybrid or private cloud environments.
  • HPC Experience:
    • Hands-on experience architecting HPC clusters, including GPU/compute nodes and HPC storage technologies.
    • Familiarity with high-speed networking (e.g., InfiniBand, Mellanox, RoCE).
    • Experience with HPC cluster management tools such as HPE Cluster Management or NVIDIA Base Command Manager.
  • Infrastructure & Hardware Stack:
    • A strong foundation in Linux system administration (RHEL / SLES /Ubuntu) and virtualization.
    • A primary focus on HPE servers and storage; experience with Dell, Lenovo, Supermicro, NVIDIA DGX/HGX platforms is also acceptable.
  • AI Platform Enablement:
    • Experience deploying and supporting NVIDIA AI Enterprise and related AI/ML frameworks.
    • Familiarity with DevOps/MLOps practices, including CI/CD, Infrastructure as Code, and cloud-native security.

Requirements:

  • Demonstrated success in delivering infrastructure projects, including platform build-outs for AI/ML and HPC workloads.
  • The ability to collaborate effectively with cross-functional teams and align technical solutions with business objectives.
  • Candidates with strong Kubernetes or HPC and AI/ML experience but limited infrastructure hardware exposure may be considered if they demonstrate trainability in server and platform environments.

Scope of Work:

This is a delivery-focused role with limited presales involvement. The engineer will be responsible for designing and building platforms to support enterprise-grade AI workloads.



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