Solution Architect AI Infrastructure & Private Cloud

PS- Global Competency Center
Hewlett Packard Enterprise

Job Title - Solution Architect AI infrastructure & Private Cloud

Job Description:
We are seeking an experienced Solution Architect AI infrastructure &
Private Cloud 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.