Solutions Architect – HPC & AI Infrastructure

GTN Technical Staffing; Dallas (TX); Hybrid; USD 140,000 - 190,000; Full time

Benefits offered by this job

Hybrid work environment

Relocation package available

Job summary

GTN Technical Staffing is seeking a Solutions Architect for HPC & AI Infrastructure in a hybrid Dallas, TX setting. You will lead technical design, integration, and delivery of scalable HPC and AI infrastructure solutions, spanning GPU/CPU compute, storage, networking, Kubernetes, orchestration, and security.

The role requires deep HPC expertise, hands-on system design, performance tuning, and the ability to translate complex customer requirements into production-ready architectures.

Qualifications

• Strong expertise in GPU and CPU architectures and the NVIDIA CUDA ecosystem.
• Experience with Slurm, Kubernetes, and container orchestration.
• Knowledge of high-performance networking technologies including InfiniBand, RDMA, and RoCE.
• Experience with storage platforms such as Lustre, GPFS/Spectrum Scale, Ceph, VAST, or comparable systems.
• Knowledge of identity, encryption, and infrastructure security.
• Strong Linux systems knowledge, including tuning and performance analysis.
• Experience translating workload requirements into detailed technical architectures.
• Experience with proof-of-concept, benchmarking, or workload optimization.
• Strong customer-facing communication and presentation skills.
• Ability to work effectively with engineering, product, operations, and executive stakeholders.

Responsibilities

• Design end-to-end HPC and AI architectures across compute, storage, networking, orchestration, and security.
• Lead technical discovery sessions to understand workload requirements and performance targets.
• Provide technical leadership during deployment and integration.
• Develop architecture blueprints, integration plans, and technical documentation.
• Support solutions from architecture through production deployment and optimization.
• Troubleshoot complex infrastructure and workload issues during delivery.
• Lead customer workshops, architecture reviews, and technical presentations.

Skills

GPU/CPU compute architectures

Kubernetes

Security

Linux performance analysis

Customer-facing communication

Workload profiling

Education

Bachelor’s or Master’s in Computer Science, Engineering, Physics, or related field

Tools

Slurm

Kubernetes

InfiniBand

RDMA

RoCE

Lustre

GPFS/Spectrum Scale

Ceph

VAST

Job description

Solutions Architect – HPC & AI Infrastructure

Work Arrangement: Hybrid

Employment Type: Direct Hire

Compensation: Competitive Base Salary + Bonus

Relocation: Relocation Package Available for Non-Local Candidates

Overview

Our client is seeking a Solutions Architect – HPC & AI Infrastructure to lead the technical design, integration, and delivery of high-performance computing and AI infrastructure solutions.

This highly technical, customer-facing role focuses on designing scalable architectures across GPU/CPU compute, storage, networking, Kubernetes, orchestration, and security. The position spans the full solution lifecycle, from technical discovery and workload analysis through proof-of-concept, deployment, and ongoing optimization.

The ideal candidate brings deep HPC and AI infrastructure expertise, strong hands-on system design and performance tuning experience, and the ability to translate complex customer requirements into scalable, production-ready solutions.

Key Responsibilities

Customer Engagement & Technical Discovery

• Work directly with customers to understand workload requirements, performance targets, and technical objectives.
• Lead technical discovery sessions focused on application behavior, bottlenecks, scalability, and infrastructure requirements.
• Serve as a trusted technical advisor throughout the solution lifecycle.
• Design end-to-end HPC and AI architectures across compute, storage, networking, orchestration, and security.
• Recommend hardware and software solutions aligned with performance, scalability, reliability, and efficiency goals.
• Develop architecture blueprints, integration plans, and technical documentation.
• Design solutions supporting GPU-intensive AI/ML, LLM, and advanced compute workloads.

Performance & Workload Optimization

• Support proof-of-concept, benchmarking, and performance-validation initiatives.
• Perform workload profiling, system tuning, and infrastructure optimization.
• Identify bottlenecks across compute, storage, networking, and orchestration layers.
• Recommend improvements that increase workload performance, scalability, and resilience.

Implementation & Delivery

• Provide technical leadership during deployment and integration.
• Partner with customers and internal Engineering, Product, and Operations teams throughout implementation.
• Support solutions from architecture through production deployment and optimization.
• Troubleshoot complex infrastructure and workload issues during delivery.

Technical Leadership

• Maintain expertise across emerging HPC, AI, GPU, storage, networking, and orchestration technologies.
• Build relationships with technology partners across GPU, networking, and storage ecosystems.
• Contribute to reference architectures, reusable design patterns, and technical best practices.
• Lead customer workshops, architecture reviews, and technical presentations.

Required Qualifications

• Strong technical expertise across GPU and CPU architectures and the NVIDIA/CUDA ecosystem.
• Experience with Slurm, Kubernetes, and container orchestration.
• Knowledge of high-performance networking technologies, including InfiniBand, RDMA, and RoCE.
• Experience with storage platforms such as Lustre, GPFS/Spectrum Scale, Ceph, VAST, or comparable systems.
• Knowledge of identity, encryption, and infrastructure security.
• Strong Linux systems knowledge, including tuning and performance analysis.
• Experience translating workload requirements into detailed technical architectures.
• Experience with proof-of-concept, benchmarking, or workload optimization.
• Strong customer-facing communication and presentation skills.
• Ability to work effectively with engineering, product, operations, and executive stakeholders.

Preferred Qualifications

• Experience supporting AI/ML, LLM, GPU, or HPC workloads.
• Experience with NVIDIA GPU infrastructure.
• Experience with containerized HPC environments.
• Familiarity with automation and Infrastructure as Code.
• Experience with workload migration and performance engineering.
• Knowledge of next-generation GPU and high-speed interconnect technologies.
• Bachelor’s or master’s degree in Computer Science, Engineering, Physics, or a related field.
• Relevant cloud, Linux, networking, Kubernetes, or security certifications.