Senior AI Compute Engineer

Job Description

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.