AI/HPC Infrastructure Engineer: GPU Compute & Hybrid Cloud

Saigepartners

San Jose (CA)

Hybrid

USD 120,000 - 180,000

Full time

3 days ago

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Job summary

Saige Partners is seeking an AI/HPC Systems Engineer to build, deploy, and operate GPU-enabled compute infrastructure for AI and HPC workloads across on-premises and cloud. The role emphasizes Linux, GPU/HPC environments, and automation.

You will collaborate with engineering and IT teams to deliver scalable, reliable systems. The successful candidate will work across HPC, GPU computing, cloud infrastructure, and AI platforms, aligning with engineering and R&D needs to support evolving technology

Qualifications

Bachelor's degree in Computer Science, Engineering, or a related technical field.
3+ years of hands-on experience in IT infrastructure, cloud engineering, platform engineering, HPC, or a related field.
Hands-on experience with Linux-based infrastructure and public cloud platforms such as AWS, Azure, or GCP.
Experience deploying, configuring, or operating GPU and/or HPC environments.
Experience with workload scheduling or orchestration technologies such as Kubernetes, Slurm, or similar platforms.
Experience with infrastructure automation, monitoring, troubleshooting, and performance optimization.
Strong understanding of compute, storage, networking, virtualization, and container technologies.
Experience supporting AI/ML infrastructure or workloads is a plus.
Strong problem-solving, collaboration, and communication skills.
Ability to work effectively across engineering, R&D, and IT teams.

Responsibilities

GPU/HPC Infrastructure Build, configure, and operate GPU and HPC clusters across compute, storage, and networking environments.
Support capacity planning, performance tuning, and infrastructure optimization for AI training, inference, and compute-intensive workloads.
Monitor system performance, availability, and resource utilization to ensure reliable operations.
Hybrid Cloud Infrastructure Deploy and maintain computing environments across on-premises infrastructure and public cloud platforms.
Support infrastructure modernization, expansion, and scaling initiatives for HPC and AI workloads.
Help evaluate and implement solutions that improve scalability, reliability, and cost efficiency.
Automation & Observability Implement infrastructure-as-code and automated provisioning solutions.
Develop and maintain monitoring, logging, alerting, and observability capabilities.
Automate routine infrastructure tasks and identify opportunities to improve resource utilization and operational efficiency.
AI Platform Support Deploy, integrate, and support LLM APIs, coding assistants, and AI/agent platforms used by engineering teams.
Assist with the infrastructure requirements and operational support of AI/ML workloads.
Collaborate with engineering teams to ensure AI platforms are reliable, accessible, and scalable.
Infrastructure Operations & Troubleshooting Troubleshoot and resolve infrastructure, networking, compute, storage, and platform issues.
Support day-to-day IT and infrastructure operations across engineering environments.
Develop and maintain technical documentation, standards, procedures, and operational runbooks.
Collaborate with engineering, IT, and other stakeholders to deliver reliable infrastructure solutions.