Lead Solutions Architect AI Infrastructure & Private Cloud

Job Title:

Job Summary:

Key Responsibilities:

1. Leadership & Strategy

• Lead solution design and delivery of AI/HPC platforms (OpenShift, Rancher, etc.) aligned with business goals and NVIDIA AI Factory principles.
  
• Ensure delivery assurance, stakeholder alignment, and risk management across the project lifecycle.
  

2. Architecture & Integration

• Architect containerized and HPC workloads using platforms like Red Hat OpenShift, SUSE Rancher, Slurm, and PBS Pro.
  
• Integrate NVIDIA AI Enterprise and open-source ML tools into broader software ecosystems.
  

3. Technical Engagements

• Lead RFP/RFI responses, customer consultations, and PoCs to validate performance and integration.
  
• Recommend optimal infrastructure configurations using reference architectures from clients and partners.
  

4. Innovation & Enablement

• Stay updated on trends across HPC, Kubernetes, hybrid cloud, and security to drive innovation.
  
• Mentor consultants and promote internal knowledge-sharing.
  

5. Customer-Centric Delivery

• Act as a trusted advisor, translating complex technical concepts into business value.
  
• Collaborate with infrastructure, DevOps, and data science teams for seamless delivery.
  

Required Skills & Experience:

AI Infrastructure & HPC:

• Proficiency in workload schedulers (Slurm, PBS Pro), cluster management tools (HPCM, Base Command Manager), and high-speed networking (InfiniBand, Ethernet).
  

Containers & Orchestration:

• Hands-on experience with Docker, Podman, Singularity; expertise in Kubernetes, OpenShift, Rancher.
  
• Deep knowledge of NVIDIA GPU Operator, DCGM, and GPU optimization.
  

Operating Systems & Virtualization:

• Advanced Linux admin skills (RHEL, SLES, Ubuntu) and experience with virtualization (KVM, OpenShift Virtualization).
  

Cloud, DevOps & MLOps:

• Experience with hybrid cloud environments, CI/CD, IaC, and integrating ML frameworks into production workflows.
  
• Understanding of cloud-native security, observability, and compliance.
  

Networking & Protocols:

• Strong grasp of networking (TCP/IP, DNS, routing) and protocols like S3, NFS, SMB for hybrid data workflows.
  

Programming & Automation:

• Scripting experience in Python, Bash, and automation of infrastructure/ML pipelines.
  

Soft Skills & Leadership:

• Strong communication, project leadership, and stakeholder management skills.
  
• Ability to align technical solutions with enterprise business objectives.
  

Qualifications:

• Bachelor’s/Master’s in Computer Science or related field.
  
• 8–10 years of experience in AI/ML, HPC, and container-based solutions.
  
• Preferred certifications: RHCSA, RHCE, CKA, CKAD, CKS, NVIDIA Certified – AI Infrastructure & Ops.