Description

Required Skills:

1. 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.

2. 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.

3.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.

4. 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.

5. Networking & Protocols
Strong understanding of core networking principles, including DNS, TCP/IP, routing, and load balancing, essential for designing resilient and scalable infrastructure.
Working knowledge of key network protocols, such as S3, NFS, and SMB/CIFS, for data access, transfer, and integration across hybrid environments.

6. Programming & Automation
Proficiency in scripting or programming languages such as Python and Bash.
Experience automating infrastructure and AI workflows.

7. Soft Skills & Leadership
Excellent problem-solving, analytical thinking, and communication skills for engaging both technical and non-technical stakeholders.
Proven ability to lead complex technical projects from requirements gathering through architecture, design, and delivery.
Strong business acumen with the ability to align technical solutions with client challenges and objectives.

Qualifications:
Bachelor’s/master’s degree in computer science, Information Technology, or a related field.
Professional certifications in AI Infrastructure, Containers and Kubernetes are highly desirable —such as RHCSA, RHCE, CNCF certifications (CKA, CKAD, CKS), NVIDIA-Certified Associate - AI Infrastructure and Operations
Typically, 8–10 years of hands-on experience in architecting and implementing HPC, AI/ML, and container platform solutions within hybrid or private cloud environments, with a strong focus on scalability, performance, and enterprise integration

Key Skills

Education

Bachelor's or Master's degrees