Site Reliability Engineer

Total Experience - 7+ Years

Relevant Experience- 5+ Years

Must have Experience in GPU at least 1 Year

Notice Period - up to 30 Days

JD :

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.

Key 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.

Required Qualifications