Cloud Ops Engineer

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
- 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