Lead Solutions Architect – AI Infrastructure & Private Cloud

Job Title – Lead Solutions Architect – AI Infrastructure & Private Cloud

Job Description:

We are seeking an experienced Lead Solutions Architect with deep expertise in AI/ML infrastructure High Performance Computing (HPC) and container platforms to join our dynamic team focused on delivering HPE Private Cloud AI and Enterprise AI Factory Solutions. This role is instrumental in architecting deploying and optimizing private cloud environments that leverage HPE’s co-developed solutions with NVIDIA as well as validated HPE reference architectures to support enterprise-grade AI workloads at scale.

The ideal candidate will bring strong technical expertise in AI infrastructure container orchestration platforms and hybrid cloud environments and will play a key role in delivering scalable secure and high-performance AI platform solutions powered by HPE GreenLake and NVIDIA AI Enterprise technologies.

Key Responsibilities:

1. Leadership and Strategy:

   • Provide delivery assurance and serve as the lead design authority to ensure seamless execution of Enterprise grade container platform —including Red Hat OpenShift and SUSE Rancher HPE Private Cloud AI and HPC/AI solutions fully aligned with customer AI/ML strategies and business objectives.

   • Align solution architecture with NVIDIA Enterprise AI Factory design principles including modular scalability GPU optimization and hybrid cloud orchestration.

   • Oversee planning risk management and stakeholder alignment throughout the project lifecycle to ensure successful outcomes.

2. Solution Planning and Design:

   • Architect and optimize end-to-end solutions across container orchestration and HPC workload management domains leveraging platforms such as Red Hat OpenShift SUSE Rancher and/or workload schedulers like Slurm and Altair PBS Pro.

   • Ensure seamless integration of container and AI platforms with the broader software ecosystem including NVIDIA AI Enterprise as well as open-source DevOps AI/ML tools and frameworks.

3. Opportunity assessment:

   • Lead technical responses to RFPs RFIs and customer inquiries ensuring alignment with business and technical requirements.

   • Conduct proof-of-concept (PoC) engagements to validate solution feasibility performance and integration within customer environments.

   • Assess customer infrastructure and workloads to recommend optimal configurations using validated reference architectures from HPE and strategic partners such as Red Hat NVIDIA SUSE along with components from the open-source ecosystem.

4. Innovation and Research:

   • Stay current with emerging technologies industry trends and best practices across HPC Kubernetes container platforms hybrid cloud and security to inform solution design and innovation.

5. Customer-centric mindset:

   • Act as a trusted advisor to enterprise customers ensuring alignment of AI solutions with business goals.

   • Translate complex technical concepts into value propositions for stakeholders

6. Team Collaboration:

   • Collaborate with cross-functional teams including subject matter experts in infrastructure components—such as HPE servers storage networking—and data science teams to ensure cohesive and integrated solution delivery.

   • Mentor technical consultants and contribute to internal knowledge sharing through tech talks and innovation forums.

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.

Required Skills:

architecting implementing HPC AI/ML container platform solutions cloud environments integration