Senior AI Compute Engineer

About the Role We are building next-generation AI infrastructure powering LLM training, inference clusters, and HPC workloads. As a Senior AI Compute Engineer, you will design, deploy, and operate GPU clusters based on NVIDIA and AMD GPU infrastructure-managing everything from hardware configuration and Linux optimization to Kubernetes orchestration and customer success. You will work with team end-to-end: from architecture planning and production rollout through optimization and technical support. This is hands-on infrastructure engineering at neo cloud. What you will be doing: · Deploy and manage AI GPU clusters (NVIDIA and AMD) for enterprise and cloud customers-end-to-end ownership from planning to production acceptance · Manage advanced Linux systems (RHEL, Ubuntu, Rocky) with expertise in kernel tuning, driver optimization, and system performance at scale · Build and optimize GPU infrastructure: configure CUDA, NVIDIA drivers, GPU Operator, GPUDirect RDMA, NVLink, and NVSwitch · Deploy and operate Kubernetes clusters with GPU support using Helm, Docker, and Containerd for AI workload orchestration · Configure and optimize Slurm, MPI, and parallel file systems for distributed AI training and HPC workloads · Perform root cause analysis on production incidents and proactively reduce cluster issues through validation and monitoring What we need to see: Core Compute (8+ years) · 8+ years of hands-on Linux systems administration and data center infrastructure deployment · 3+ years of HPC infrastructure experience with job schedulers (Slurm/PBS) and parallel computing · Expertise in Linux administration (RHEL, Ubuntu, Rocky)-kernel tuning, driver management, PCIe troubleshooting, performance optimization · Proficiency with GPU infrastructure (NVIDIA GPUs, CUDA, GPUDirect RDMA, NVLink, DCGM monitoring and troubleshooting) · Experience with Kubernetes and container orchestration (Helm, Docker, Containerd, GPU Operator, CSI drivers) Automation & Infrastructure-as-Code · 3+ years of infrastructure automation using Python, Bash, Ansible, Terraform, or SaltStack · Ability to develop provisioning workflows, CI/CD pipelines, and version control with Git · Strong scripting skills to automate deployment, validation, and operational tasks at scale Ways to stand out from the rest: · NVIDIA certifications (AI Infrastructure, AI Operations, Certified Associate/Professional) · Kubernetes certifications (CKA, CKS) or Red Hat Certified Engineer (RHCE) · Experience with AI Factory deployments, LLM training clusters, or GPU cloud platforms · Background with NVIDIA DGX SuperPOD, HGX clusters, or NVIDIA Spectrum-X networking · Experience with monitoring stacks (Prometheus, Grafana, DCGM, ELK, Loki) and observability in distributed systems · Hands-on experience with advanced storage systems (Ceph, GPFS, Weka, VAST) or bare-metal provisioning (MAAS, Foreman) Minimum Qualifications: · Bachelor's degree in Computer Science, Electrical Engineering, Electronics, Information Technology, or equivalent professional experience · 8+ years of Linux systems administration and data center deployment · 4+ years of consulting or customer-success engineering roles Soft Skills: · Strong problem-solving and debugging abilities across hardware, kernel, and application layers · Ownership mindset with accountability for deployment quality and customer success · Cross-functional collaboration with other teams · Proactive approach to continuous learning and staying current with AI infrastructure trends · Strong documentation and presentation skills, able to defend design decisions amongst peers.