Lead Solutions Architect - AI Infrastructure & Private Cloud

Required Skills:

• HPC & AI Infrastructure
• Extensive knowledge of HPC technologies and workload scheduler such as Slurm and/or Altair PBS Pro,
• Proficient in HPC cluster management tools, including HPE Cluster Management (HPCM) and/or NVIDIA Base Command Manager.
• Experience with HPC cluster managers like HPE Cluster Management (HPCM) and/or NVIDIA Base Command Manager.
• Good understanding with high-speed networking stacks (InfiniBand, Mellanox) and performance tuning of HPC components.
• Solid grasp of high-speed networking technologies, such as InfiniBand and Ethernet.

Containerization & Orchestration

• Extensive hands-on experience with containerization technologies such as Docker, Podman, and Singularity
• Proficiency with at least two container orchestration platforms: CNCF Kubernetes, Red Hat OpenShift, SUSE Rancher (RKE/K3S), Canonical Charmed Kubernetes.
• Strong understanding of GPU technologies, including the NVIDIA GPU Operator for Kubernetes-based environments and DCGM (Data Center GPU Manager) for GPU health and performance monitoring.

Operating Systems & Virtualization

• Extensive experience in Linux system administration, including package management, boot process troubleshooting, performance tuning, and network configuration.
• Proficient with multiple Linux distributions, with hands-on expertise in at least two of the following: RHEL, SLES, and Ubuntu.
• Experience with virtualization technologies, including KVM and OpenShift Virtualization, for deploying and managing virtualized workloads in hybrid cloud environments.

Cloud, DevOps & MLOps

• Solid understanding of hybrid cloud architectures and experience working with major cloud platforms in conjunction with on-premises infrastructure.
• Familiarity with DevOps practices, including CI/CD pipelines, infrastructure as code (IaC), and microservices-based application delivery.
• Experience integrating and operationalizing open-source AI/ML tools and frameworks, supporting the full model lifecycle from development to deployment.
• Good understanding of cloud-native security, observability, and compliance frameworks, ensuring secure and reliable AI/ML operations at scale.

Networking & Protocols

• Strong understanding of core networking principles, including DNS, TCP/IP, routing, and load balancing, essential for designing resilient and scalable infrastructure.