What are the responsibilities and job description for the Senior Solution Architect – AI / GPU Cloud position at Glint Tech Solutions?
Role Overview
GMI Cloud is seeking a Senior Solution Architect – AI / GPU Cloud to act as a trusted technical advisor
for enterprise and hyperscaler customers. This role sits at the intersection of AI infrastructure, GPU
cloud architecture, and customer-facing solution design, enabling large-scale AI/ML and HPC
workloads.
Key Responsibilities
Customer Engagement & Technical Leadership
Serve as the primary technical point of contact for enterprise and hyperscaler customers
Understand customer AI/ML/HPC workloads, scaling requirements, and deployment models
Architect end-to-end GPU cloud solutions including compute, storage, networking, and orchestration
Solution Design & Proof of Concept (PoC)
Lead PoCs, benchmarks, and technical workshops
Produce architecture diagrams, capacity plans, and cost/performance analyses
Translate complex technical challenges into actionable plans
Deployment & Enablement
Guide onboarding, cluster setup, tuning, and scaling
Partner with Infrastructure, Data Center Ops, and Engineering teams
Identify optimization opportunities across GPU utilization and cost
Ongoing Support & Relationship Building
Act as a long-term technical advisor on AI/GPU infrastructure
Conduct regular technical and capacity reviews
Collaborate with product and engineering to improve the platform
Required Qualifications
Technical Background
5–10 years in cloud infrastructure, GPU cloud, HPC, or AI/ML infrastructure
Experience with distributed training/inference, Kubernetes or Slurm
Strong knowledge of NVIDIA GPU stack (H100/H200/B200/GB200)
Familiarity with InfiniBand and AI-optimized storage architectures
Customer-Facing & Soft Skills
Experience working directly with enterprise or hyperscaler customers
Strong communication and solution design skills
Self-starter mindset with strong ownership and problem-solving ability
GMI Cloud is seeking a Senior Solution Architect – AI / GPU Cloud to act as a trusted technical advisor
for enterprise and hyperscaler customers. This role sits at the intersection of AI infrastructure, GPU
cloud architecture, and customer-facing solution design, enabling large-scale AI/ML and HPC
workloads.
Key Responsibilities
Customer Engagement & Technical Leadership
Serve as the primary technical point of contact for enterprise and hyperscaler customers
Understand customer AI/ML/HPC workloads, scaling requirements, and deployment models
Architect end-to-end GPU cloud solutions including compute, storage, networking, and orchestration
Solution Design & Proof of Concept (PoC)
Lead PoCs, benchmarks, and technical workshops
Produce architecture diagrams, capacity plans, and cost/performance analyses
Translate complex technical challenges into actionable plans
Deployment & Enablement
Guide onboarding, cluster setup, tuning, and scaling
Partner with Infrastructure, Data Center Ops, and Engineering teams
Identify optimization opportunities across GPU utilization and cost
Ongoing Support & Relationship Building
Act as a long-term technical advisor on AI/GPU infrastructure
Conduct regular technical and capacity reviews
Collaborate with product and engineering to improve the platform
Required Qualifications
Technical Background
5–10 years in cloud infrastructure, GPU cloud, HPC, or AI/ML infrastructure
Experience with distributed training/inference, Kubernetes or Slurm
Strong knowledge of NVIDIA GPU stack (H100/H200/B200/GB200)
Familiarity with InfiniBand and AI-optimized storage architectures
Customer-Facing & Soft Skills
Experience working directly with enterprise or hyperscaler customers
Strong communication and solution design skills
Self-starter mindset with strong ownership and problem-solving ability