Job description

Job Requirements Responsibilities: • GPU/HPC infrastructure: Build, configure, and operate GPU and HPC clusters across compute, storage, and networking; support capacity planning, performance tuning, and optimization for AI training, inference, and compute-intensive workloads • Hybrid cloud infrastructure: Deploy and maintain compute environments spanning on-premise and public cloud, and contribute to modernization and scaling initiatives for HPC/AI infrastructure • Automation and observability: Implement infrastructure-as-code, provisioning automation, monitoring, and alerting, and drive improvements in resource utilization and efficiency • AI platform support: Deploy, integrate, and support LLM APIs, coding assistants, and AI/agent platforms used by internal engineering teams • Operations and collaboration: Troubleshoot and resolve infrastructure issues, document standards and runbooks, and work with relevant stakeholders to support day-to-day IT operations RequirementsQualifications: • Bachelor's degree in Computer Science, Engineering, or a related technical field • 3+ years of hands-on experience in IT infrastructure, cloud, platform engineering, or HPC • Hands-on experience with Linux-based infrastructure and public cloud environments such as AWS, Azure, or GCP • Experience deploying or operating GPU/HPC environments, including workload scheduling or orchestration platforms such as Kubernetes or Slurm • Experience with infrastructure automation, monitoring, troubleshooting, and performance optimization • Solid understanding of compute, storage, networking, and container technologies; experience with AI/ML infrastructure or workloads is a plus • Strong collaboration and communication skills, with the ability to work across engineering and IT teams

Requirements

Must-haves

Job Requirements
Hybrid cloud infrastructure: Deploy and maintain compute environments spanning on-premise and public cloud, and contribute to modernization and scaling initiatives for HPC/AI infrastructure
Automation and observability: Implement infrastructure-as-code, provisioning automation, monitoring, and alerting, and drive improvements in resource utilization and efficiency
AI platform support: Deploy, integrate, and support LLM APIs, coding assistants, and AI/agent platforms used by internal engineering teams
Operations and collaboration: Troubleshoot and resolve infrastructure issues, document standards and runbooks, and work with relevant stakeholders to support day-to-day IT operations
Bachelor's degree in Computer Science, Engineering, or a related technical field
3+ years of hands-on experience in IT infrastructure, cloud, platform engineering, or HPC
Hands-on experience with Linux-based infrastructure and public cloud environments such as AWS, Azure, or GCP
Experience deploying or operating GPU/HPC environments, including workload scheduling or orchestration platforms such as Kubernetes or Slurm
Experience with infrastructure automation, monitoring, troubleshooting, and performance optimization
Strong collaboration and communication skills, with the ability to work across engineering and IT teams

Nice-to-haves

Solid understanding of compute, storage, networking, and container technologies; experience with AI/ML infrastructure or workloads is a plus

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Keywords to mirror

aillmawsazuregcpkubernetesml

Platform: phenom
Effort: medium

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