Lead Solutions Architect AI Infrastructure & Private Cloud

Location : Bengaluru
PS- Global Competency Center
Hewlett Packard Enterprise
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.