New opportunity

Sr. Solutions Architect

Penguin Solutions · Singapore

About this role

Penguin Solutions is seeking a Sr. Solutions Architect to serve as a lead technical visionary in the pre-sales process. This role involves acting as a trusted advisor to clients and a technical partner to the sales team, translating complex customer challenges into end-to-end AI and HPC solutions. Responsibilities include leading technical responses for RFIs and RFPs, designing architectures that meet mission and security requirements, and acting as a liaison between sales and internal engineering teams. The role requires significant travel to support customer meetings and workshops. Required Qualifications: 7+ years of experience in a customer-facing, pre-sales technical role; deep hands-on experience designing large-scale enterprise systems in AI/ML, HPC, or Big Data; exceptional presentation and communication skills; and a strong understanding of the interplay between software, hardware, and networking in data centers. Preferred Qualifications: Bachelor’s degree in Computer Science or Electrical Engineering; deep knowledge of AI/HPC architectures, Linux clusters, and Data Center Infrastructure; experience with NeoCloud environments; and familiarity with file systems and software-defined storage solutions such as Lustre, VAST, or WekaIO.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Solutions ArchitecturePre-sales EngineeringHPCAI/ML InfrastructureCloud ComputingData Center InfrastructureLinux ClustersEnterprise Systems DesignTechnical PresentationsFile SystemsSoftware Defined StorageLustreGlusterGPFS/ESSVASTWekaIOCEPHSWIFTCinderNeoCloud

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

End-to-End AI/HPC Architecture Design
The role requires translating complex customer needs into feasible, large-scale AI and HPC infrastructure designs. You must demonstrate deep expertise in the interplay between hardware, software, and networking within modern data centers to ensure solutions are both performant and aligned with mission requirements.
Technical Pre-Sales Leadership
Acting as the lead technical visionary, you must manage the technical response for RFIs and RFPs. This involves balancing customer requirements with internal product capabilities and serving as the primary liaison between sales, engineering, and product management to ensure proposed solutions are deliverable.
Strategic Customer Advisory
You are expected to act as a trusted advisor to clients, requiring the ability to articulate the value proposition of Penguin Solutions against competitors. This involves delivering high-level architecture reviews and technical briefings to both technical and non-technical stakeholders.

Likely challenges to think through

Balancing Customization with Scalability
Designing bespoke solutions for specific mission requirements while maintaining the standard of a scalable 'AI Factory' platform can create friction. Prepare by being ready to discuss how you maintain design intent while ensuring internal operations and engineering teams can reliably deploy and support the final architecture.
Navigating Complex Stakeholder Environments
The role involves working with prime contractors, integrators, and direct agency engagements. You may face conflicting priorities between these parties. Prepare by highlighting your experience in managing expectations across diverse stakeholder groups and your ability to maintain technical integrity during high-pressure procurement cycles.

Practice your answers

Technical practice

Designing Large-Scale AI/HPC Clusters

What to coverExplain the critical considerations when designing a Linux-based HPC cluster for AI training workloads. Focus on the interplay between compute nodes, high-speed interconnects, and the impact of data center power and cooling constraints.

Software-Defined Storage for AI Workloads

What to coverCompare and contrast parallel file systems like Lustre with modern software-defined storage solutions like VAST or WekaIO. Discuss how these choices affect throughput, latency, and scalability for large-scale AI training and inference.

Networking Architectures in Data Centers

What to coverDescribe the networking requirements for a modern AI factory, specifically regarding RDMA, InfiniBand, or high-speed Ethernet. Address how you ensure low-latency communication between GPU nodes to prevent bottlenecks.

Translating Requirements to Architecture

What to coverWhen presented with an RFP for a secure, regulated enterprise environment, how do you balance customer mission requirements with technical feasibility? Suggestion: Focus on your methodology for mapping compliance standards to specific hardware and software configurations.

Competitive Positioning of AI Solutions

What to coverHow do you articulate the value proposition of a full-stack AI factory platform compared to fragmented, multi-vendor cloud or on-premise deployments? Focus on the benefits of integrated hardware, software, and services.

Liaison Between Sales and Engineering

What to coverDescribe your process for ensuring that a proposed architecture is not only compelling to the customer but also deliverable by internal engineering and operations teams. Discuss how you manage technical debt and design intent during the handover.

NeoCloud and Hybrid Infrastructure

What to coverExplain the architectural challenges of integrating on-premise HPC clusters with NeoCloud or other cloud environments. Discuss data gravity, security, and consistent management across these hybrid footprints.

Managing Concurrent Design Projects

What to coverIn a fast-paced pre-sales environment, how do you prioritize multiple RFI/RFP responses while maintaining high-quality technical documentation? Suggestion: Discuss your approach to modularizing designs and leveraging reusable architectural patterns.

Behavioural practice

Handling Technical Objections

What to coverDescribe a situation where a client challenged your proposed technical architecture during a presentation. How did you address their concerns while maintaining the relationship and the integrity of the solution?

Influencing Stakeholders Without Authority

What to coverProvide an example of how you persuaded internal product or engineering teams to modify a roadmap or design to better meet a specific customer's critical requirement. Focus on the data or evidence you used to build your case.

Simplifying Complex Technical Concepts

What to coverTell me about a time you had to explain a highly complex AI infrastructure concept to a non-technical stakeholder or executive. How did you adjust your communication style to ensure they understood the business impact?

Managing Through Ambiguity

What to coverDescribe a project where the customer's requirements were initially vague or incomplete. How did you work with the sales team and the client to refine the scope and deliver a successful, feasible architecture?

Sharpen your knowledge

Topics to prepare

AI and HPC Architecture Design
Review the integration of compute, memory, and networking for large-scale AI training and inference workloads. Exercise: Draft a high-level architecture diagram for a multi-node GPU cluster, detailing the interconnects and data flow. This is relevant to the role's requirement to design end-to-end AI factory solutions.
Pre-Sales Technical Strategy
Understand the lifecycle of RFI/RFP responses and the role of a technical advisor in qualifying opportunities. Exercise: Practice articulating the competitive advantages of a specific storage or compute stack against a hypothetical alternative. This is essential for the role's focus on leading technical responses and competitive positioning.
Data Center Infrastructure Interplay
Study the dependencies between power, cooling, rack density, and software-defined infrastructure. Exercise: Create a checklist of constraints to consider when deploying high-density GPU clusters in a legacy data center. This aligns with the requirement to understand the interplay between hardware, software, and networking.

Skills to revise

Software-Defined Storage (SDS) Proficiency
Gain familiarity with the architecture and use cases of Lustre, VAST, or WekaIO. Exercise: Compare the performance characteristics and scalability of parallel file systems versus object storage for AI training datasets. This is a preferred qualification for the role.
Technical Presentation and Whiteboarding
Develop the ability to simplify complex technical concepts for diverse stakeholders. Exercise: Prepare a 10-minute whiteboard session explaining the value proposition of a specific HPC cluster design to a non-technical decision-maker. This is critical for the customer-facing nature of the position.
Linux Cluster Management
Deepen knowledge of Linux-based cluster orchestration and monitoring. Exercise: Review common bottlenecks in large-scale Linux clusters and how they impact job scheduling and throughput. This is directly relevant to the preferred qualifications for managing AI/HPC architectures.

Your preparation checklist

Prepare a technical case study on AI/HPC infrastructure
Develop a presentation detailing a large-scale system you designed, focusing on the integration of compute, networking, and storage. Highlight how you balanced performance requirements with hardware constraints in an AI/ML or HPC environment.
Create a whiteboarding exercise for RFP response
Practice mapping a complex customer requirement to a high-level architecture diagram. Prepare to explain your design choices regarding software-defined storage (e.g., Lustre or WekaIO) and how they address specific mission-critical security or performance needs.
Draft a competitive analysis summary
Prepare a concise comparison of current AI factory infrastructure trends, specifically contrasting Penguin Solutions' approach with alternative cloud or on-premise offerings. Be ready to articulate the value proposition of integrated hardware and software stacks.
Formulate strategic questions for the interview
Prepare questions regarding the typical sales cycle for their AI factory projects, the collaboration process between pre-sales and internal engineering teams, and the specific technical challenges currently faced by their NeoCloud customers.
Review documentation for storage and cluster technologies
Refresh your technical knowledge on Linux cluster management and modern file systems like VAST, CEPH, or GPFS. Ensure you can discuss the interplay between these storage solutions and high-performance GPU computing workloads.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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