AI Infrastructure Engineer

Role Overview

As an AI Infrastructure Engineer, you will be responsible for the end-to-end deployment of on-premise AI solutions. This role is a unique blend of high-performance hardware engineering and software stack orchestration. You will be the driving force behind the physical assembly of high-performance computing hardware, operating system provisioning, and the deployment of containerized AI applications.

You will serve as the primary technical point of contact for ensuring system reliability and performance at client sites, bridging the gap between raw silicon and production-ready AI models.

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Key Responsibilities

• Systems Integration: Perform full-cycle hardware assembly, including the integration of high-performance Motherboards (MB), CPUs, GPUs, and high-speed NVMe/SSD storage arrays.
• Logistics & Hardware Management: Oversee the end-to-end logistics lifecycle of hardware assets, including inventory tracking, shipping, and coordinated onsite installation to ensure seamless customer delivery.
• Deployment & Maintenance: Provision and configure Linux-based environments and specialized AI software stacks on-premise, ensuring environments are tuned for heavy compute.
• Container Management: Deploy and manage AI applications using Docker, ensuring containers are optimized, resource-constrained, and running according to specification.
• System Diagnostics: Monitor system health and perform deep-dive debugging by analyzing Docker logs, kernel logs (dmesg), and hardware telemetry to preempt failures.
• Collaborative Troubleshooting: Isolate and resolve complex network configurations, driver conflicts (NVIDIA/CUDA), and hardware-level failures.

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Minimum Qualifications

• Education: Bachelor’s degree in Computer Science, Computer Engineering, or a related technical field.
• Experience: 1–2 years in infrastructure support, system administration, or similar roles. Fresh graduates with strong Linux and "homelab" infrastructure knowledge are encouraged to apply.
• Hardware Proficiency: Deep understanding of PC/Server architecture, including CPU architectures, GPU power requirements, PCIe lane distribution, and DRAM/Storage scaling.
• OS & Scripting: Strong command of the Linux CLI (Ubuntu/CentOS) for filesystem management, networking configuration, and shell scripting for task automation.
• Containerization: Practical experience managing Docker environments and understanding container lifecycles.
• Precision & Detail: High level of precision in physical assembly, including cable management, thermal application, and firmware/BIOS updates.

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Nice-to-Haves

• Orchestration: Basic understanding of Kubernetes or other container orchestration platforms.
• NVIDIA Stack: Familiarity with the NVIDIA AI Enterprise stack and GPU operator deployments.
• Communication: Ability to translate complex technical issues into clear status reports for both internal engineering teams and non-technical client stakeholders.