Indotronix UK
Overview
Location : US Remote We are seeking a skilled DevOps and AI Cloud Infrastructure Engineer to provision, deploy, manage, and optimize our GPU-based compute environment, ensuring high availability, performance, and security for compute-intensive workloads. The ideal candidate will have expertise in Linux system administration, cloud platforms, containerization, GPU hardware management, and cluster computing, with a focus on supporting AI/ML and high-performance computing (HPC) workloads. In this role, you will also provide technical support to investigate and resolve customer-reported issues related to the GPU-based compute environment. You will work closely with architects, AI engineers, and software developers to ensure seamless deployment, scalability, and reliability of our cloud-based AI/ML pipelines and GPU-based compute environments. Responsibilities
Infrastructure Management: Provision, deploy, and maintain scalable, secure, and high-availability cloud infrastructure on platforms such as Digital Ocean Cloud to support AI workloads. Documentation: Maintain clear documentation for infrastructure setups, and processes. System Management: Administer and maintain Linux-based servers and clusters optimized for GPU compute workloads, ensuring high availability and performance. GPU Infrastructure: Configure, monitor, and troubleshoot GPU hardware (e.g., NVIDIA GPUs) and related software stacks (e.g., CUDA, cuDNN) for optimal performance in AI/ML and HPC applications. Troubleshooting: Diagnose and resolve hardware and software issues related to GPU compute nodes and performance issues in GPU clusters. High-Speed Interconnects: Implement and manage high-speed networking technologies like RDMA over Converged Ethernet (RoCE) to support low-latency, high-bandwidth communication for GPU workloads. Automation: Develop and maintain Infrastructure as Code (IaC) using tools like Terraform, Ansible to automate provisioning and management of resources. CI/CD Pipelines: Build and optimize continuous integration and deployment (CI/CD) pipelines for testing GPU-based servers and managing deployments using tools like GitHub Actions. Containerization & Orchestration: Build and manage LXC-based containerized environments to support cloud infrastructure and provisioning toolchains. Monitoring & Performance: Set up and maintain monitoring, logging, and alerting systems (e.g., Prometheus, Victoria Metrics, Grafana) to track system performance, GPU utilization, resource bottlenecks, and uptime of GPU resources. Security and Compliance: Implement network security measures, including firewalls, VLANs, VPNs, and intrusion detection systems, to protect the GPU compute environment and comply with standards like SOC 2 or ISO 27001. Cluster Support: Collaborate with other engineers to ensure seamless integration of networking with cluster management tools like Slurm, or PBS Pro. Scalability: Optimize infrastructure for high-throughput AI workloads, including GPU and auto-scaling configurations. Collaboration: Work closely with Architects, Software engineers to streamline model deployment, optimize resource utilization, and troubleshoot infrastructure issues. Qualifications
Experience: 3+ years of experience in DevOps, Site Reliability Engineering (SRE), or cloud infrastructure management, with at least 1 year working on GPU-based compute environments in the cloud.
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Location : US Remote We are seeking a skilled DevOps and AI Cloud Infrastructure Engineer to provision, deploy, manage, and optimize our GPU-based compute environment, ensuring high availability, performance, and security for compute-intensive workloads. The ideal candidate will have expertise in Linux system administration, cloud platforms, containerization, GPU hardware management, and cluster computing, with a focus on supporting AI/ML and high-performance computing (HPC) workloads. In this role, you will also provide technical support to investigate and resolve customer-reported issues related to the GPU-based compute environment. You will work closely with architects, AI engineers, and software developers to ensure seamless deployment, scalability, and reliability of our cloud-based AI/ML pipelines and GPU-based compute environments. Responsibilities
Infrastructure Management: Provision, deploy, and maintain scalable, secure, and high-availability cloud infrastructure on platforms such as Digital Ocean Cloud to support AI workloads. Documentation: Maintain clear documentation for infrastructure setups, and processes. System Management: Administer and maintain Linux-based servers and clusters optimized for GPU compute workloads, ensuring high availability and performance. GPU Infrastructure: Configure, monitor, and troubleshoot GPU hardware (e.g., NVIDIA GPUs) and related software stacks (e.g., CUDA, cuDNN) for optimal performance in AI/ML and HPC applications. Troubleshooting: Diagnose and resolve hardware and software issues related to GPU compute nodes and performance issues in GPU clusters. High-Speed Interconnects: Implement and manage high-speed networking technologies like RDMA over Converged Ethernet (RoCE) to support low-latency, high-bandwidth communication for GPU workloads. Automation: Develop and maintain Infrastructure as Code (IaC) using tools like Terraform, Ansible to automate provisioning and management of resources. CI/CD Pipelines: Build and optimize continuous integration and deployment (CI/CD) pipelines for testing GPU-based servers and managing deployments using tools like GitHub Actions. Containerization & Orchestration: Build and manage LXC-based containerized environments to support cloud infrastructure and provisioning toolchains. Monitoring & Performance: Set up and maintain monitoring, logging, and alerting systems (e.g., Prometheus, Victoria Metrics, Grafana) to track system performance, GPU utilization, resource bottlenecks, and uptime of GPU resources. Security and Compliance: Implement network security measures, including firewalls, VLANs, VPNs, and intrusion detection systems, to protect the GPU compute environment and comply with standards like SOC 2 or ISO 27001. Cluster Support: Collaborate with other engineers to ensure seamless integration of networking with cluster management tools like Slurm, or PBS Pro. Scalability: Optimize infrastructure for high-throughput AI workloads, including GPU and auto-scaling configurations. Collaboration: Work closely with Architects, Software engineers to streamline model deployment, optimize resource utilization, and troubleshoot infrastructure issues. Qualifications
Experience: 3+ years of experience in DevOps, Site Reliability Engineering (SRE), or cloud infrastructure management, with at least 1 year working on GPU-based compute environments in the cloud.
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