Chief Cloud Solutions Architect

14 hours ago

Erode, Tamil Nadu, India beBeeSolution Full time ₹ 1,50,00,000 - ₹ 2,00,00,000

Job Title:

Solution Architect

Role Description:

As a delivery-focused professional, you will design and build platforms to support enterprise-grade AI workloads.

Core Focus Areas

  • Kubernetes Platforms:
    • Proficiency in container orchestration platforms, especially Red Hat OpenShift, SUSE Rancher, and CNCF Kubernetes.
    • Experience with GPU-accelerated workloads and tools like NVIDIA GPU Operator and DCGM.
    • Proven ability to integrate Kubernetes with AI/ML workloads and GPU infrastructure in hybrid or private cloud environments.
  • HPC Experience:
    • Experience architecting HPC clusters, including GPU/compute nodes and HPC storage technologies (e.g., Lustre, WEKA, Parallel Filesystems).
    • Understanding of high-speed networking (e.g., InfiniBand, Mellanox, RoCE).
    • Experience with HPC cluster management tools such as HPE Cluster Management (HPCM) or NVIDIA Base Command Manager.
    • Familiarity with HPC workload schedulers like Slurm or Altair PBS Pro.
  • Infrastructure & Hardware Stack:
    • Strong background in Linux system administration (RHEL / SLES / Ubuntu) and virtualization (KVM or OpenShift Virtualization).
    • Primary focus on HPE servers and storage; experience with Dell, Lenovo, Supermicro, NVIDIA DGX/HGX platforms is acceptable.
    • Solid understanding of compute, storage, and networking fundamentals in enterprise environments.
  • 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 (IaC), and cloud-native security.

Requirements:

  • Demonstrated success in infrastructure project delivery, including platform build-outs for AI/ML and HPC workloads.
  • Ability to collaborate with cross-functional teams and align technical solutions with business goals.
  • Trainability clause: 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.

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