Senior AI Compute Engineer

Senior AI Compute Engineer

Deploy, Optimize & Operate Next-Generation AI Infrastructure at Scale

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:

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Deploy and manage AI GPU clusters (NVIDIA and AMD) for enterprise and cloud customers—end-to-end ownership from planning to production acceptance

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Manage advanced Linux systems (RHEL, Ubuntu, Rocky) with expertise in kernel tuning, driver optimization, and system performance at scale

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Build and optimize GPU infrastructure: configure CUDA, NVIDIA drivers, GPU Operator, GPUDirect RDMA, NVLink, and NVSwitch

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Deploy and operate Kubernetes clusters with GPU support using Helm, Docker, and Containerd for AI workload orchestration

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Configure and optimize Slurm, MPI, and parallel file systems for distributed AI training and HPC workloads

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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)

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8+ years of hands-on Linux systems administration and data center infrastructure deployment

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3+ years of HPC infrastructure experience with job schedulers (Slurm/PBS) and parallel computing

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Expertise in Linux administration (RHEL, Ubuntu, Rocky)—kernel tuning, driver management, PCIe troubleshooting, performance optimization

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Proficiency with GPU infrastructure (NVIDIA GPUs, CUDA, GPUDirect RDMA, NVLink, DCGM monitoring and troubleshooting)

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Experience with Kubernetes and container orchestration (Helm, Docker, Containerd, GPU Operator, CSI drivers)

Automation & Infrastructure-as-Code

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3+ years of infrastructure automation using Python, Bash, Ansible, Terraform, or SaltStack

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Ability to develop provisioning workflows, CI/CD pipelines, and version control with Git

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Strong scripting skills to automate deployment, validation, and operational tasks at scale

 

Ways to stand out from the rest:

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NVIDIA certifications (AI Infrastructure, AI Operations, Certified Associate/Professional)

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Kubernetes certifications (CKA, CKS) or Red Hat Certified Engineer (RHCE)

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Experience with AI Factory deployments, LLM training clusters, or GPU cloud platforms

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Background with NVIDIA DGX SuperPOD, HGX clusters, or NVIDIA Spectrum-X networking

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Experience with monitoring stacks (Prometheus, Grafana, DCGM, ELK, Loki) and observability in distributed systems

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Hands-on experience with advanced storage systems (Ceph, GPFS, Weka, VAST) or bare-metal provisioning (MAAS, Foreman)

Minimum Qualifications:

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Bachelor’s degree in Computer Science, Electrical Engineering, Electronics, Information Technology, or equivalent professional experience

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8+ years of Linux systems administration and data center deployment

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4+ years of consulting or customer-success engineering roles

 

Soft Skills:

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Strong problem-solving and debugging abilities across hardware, kernel, and application layers

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Ownership mindset with accountability for deployment quality and customer success

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Cross-functional collaboration with other teams

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Proactive approach to continuous learning and staying current with AI infrastructure trends

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Strong documentation and presentation skills, able to defend design decisions amongst peers.