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Tier-1 employer and metro location increase competition, but specialized GPU/AI infra and senior level moderate applicant density.
Highly specialized AI/GPU/HPC infrastructure skills limit easy transferability across unrelated industries.
Explicit 8+ years requirement plus mandatory GPU, cloud, Kubernetes, Terraform/Ansible and SRE experience creates high filtering.
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Lead design and deployment of integrated GPU-accelerated AI and HPC infrastructure architectures across compute, network, storage, platform, and software layers for edge, data center, and hybrid cloud.
Drive end-to-end solutions including discovery, reference architecture, sizing, implementation, technical solution strategy, and project lifecycle management ensuring delivery of measurable client outcomes.
Lead technical delivery, escalation support, stakeholder engagement, and mentoring across pursuits, active implementations, and transformation programs.
8-12+ years of experience in infrastructure architecture or engineering for large-scale platform design, implementation, operations, and optimization.
Experience designing or delivering GPU-accelerated platforms for AI, machine learning, or HPC workloads including distributed compute clusters and hybrid cloud environments.
Proficient with Linux system administration in production and containerized platform technologies such as Kubernetes or Red Hat OpenShift including GPU operators and CUDA container runtime.
Bachelor’s or Master’s degree in Engineering, Computer Science, or related field from an accredited university.
Experienced in architecting complex, scalable AI and HPC infrastructure with deep knowledge of container orchestration, cluster schedulers (Slurm/Kubernetes), and infrastructure automation tools (Terraform, Ansible).
Proven ability to translate business strategy into secure, cost-optimized infrastructure solutions and lead full project lifecycle with cross-functional stakeholders in pursuit and delivery phases.
Familiar with AI ecosystem partnerships, NVIDIA platform co-sell motions, and managing multi-tenant, multi-node distributed training workloads in hybrid cloud settings.