Senior AI Infrastructure Specialist
7 days ago
We are seeking an expert system administrator to lead the implementation and management of on-premises Linux servers optimized for AI/ML workloads. The ideal candidate will have extensive experience in core Linux system administration, with a strong foundation in configuring and optimizing servers for high-performance computing tasks.
Key Responsibilities:
- Support deployment and maintenance of NVIDIA GPU-accelerated systems.
- Deploy and support Kubernetes clusters across various environments and distros (e.g., RKE, OpenShift, AKS, EKS, GKE).
- Perform day-to-day system administration across compute, storage, and networking layers.
- Automate infrastructure tasks using Shell scripts, Ansible, or similar tools.
- Collaborate with DevOps, data science, and engineering teams to ensure scalable, resilient infrastructure for AI/ML workloads.
- Monitor infrastructure health and performance; participate in troubleshooting and root cause analysis.
- Extensive experience in managing, configuring, and troubleshooting Linux-based systems (e.g., RHEL, Ubuntu, CentOS, Debian) in enterprise environments, including kernel tuning, system monitoring, and performance optimization.
- Hands-on experience in deploying and configuring Linux servers for AI/ML applications, including setup of GPU-accelerated environments, storage optimization for large datasets (e.g., using RAID, LVM), and ensuring system stability under intensive computational loads.
- Expertise in tuning Linux systems for performance, including CPU/GPU resource allocation, memory management, and I/O optimization, tailored to on-premises setups handling AI/ML training and inference workloads.
- Proven ability to diagnose and resolve intricate problems in Linux environments, such as hardware failures, network bottlenecks, or software conflicts, with an emphasis on minimizing downtime in mission-critical on-premises AI/ML systems.Requirements
To succeed in this role, you will need:
- Min 7 years of experience in systems engineering or enterprise infrastructure roles.
- Understanding of enterprise storage, networking, and system monitoring tools.
- Scripting and automation experience (e.g., Bash, Python, Ansible).
- Strong communication, documentation, and troubleshooting skills.
- Comfortable working independently in a remote environment.
- Proven ability to diagnose and resolve intricate problems in Linux environments, such as hardware failures, network bottlenecks, or software conflicts, with an emphasis on minimizing downtime in mission-critical on-premises AI/ML systems.Requirements
- Expertise in tuning Linux systems for performance, including CPU/GPU resource allocation, memory management, and I/O optimization, tailored to on-premises setups handling AI/ML training and inference workloads.
- Hands-on experience in deploying and configuring Linux servers for AI/ML applications, including setup of GPU-accelerated environments, storage optimization for large datasets (e.g., using RAID, LVM), and ensuring system stability under intensive computational loads.
- Extensive experience in managing, configuring, and troubleshooting Linux-based systems (e.g., RHEL, Ubuntu, CentOS, Debian) in enterprise environments, including kernel tuning, system monitoring, and performance optimization.
- Monitor infrastructure health and performance; participate in troubleshooting and root cause analysis.
- Collaborate with DevOps, data science, and engineering teams to ensure scalable, resilient infrastructure for AI/ML workloads.
- Deploy and support Kubernetes clusters across various environments and distros (e.g., RKE, OpenShift, AKS, EKS, GKE).
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