Systems Engineer / Site Reliability Engineer (SRE) - REMOTE

Job Type: Contract
Industry: AI Infrastructure / Cloud Computing
Experience Level: Senior / Lead
Specialization: GPU Infrastructure, Linux Systems Engineering, Site Reliability Engineering

Job Summary

We are seeking an experienced Systems Engineer / Site Reliability Engineer (SRE) to design, automate, and operate the core infrastructure platform supporting high-density AI inference environments.

In this role, you will work at the intersection of bare-metal accelerator hardware, Linux systems engineering, Kubernetes orchestration, and large-scale distributed infrastructure. You will be responsible for the reliability, scalability, performance optimization, and lifecycle automation of compute infrastructure supporting large-scale GPU workloads.

The ideal candidate will have extensive experience in Linux systems engineering, hyperscale infrastructure, GPU computing environments, and production-grade automation. You should have hands-on expertise with NVIDIA drivers, CUDA, Kubernetes, infrastructure as code, and automated provisioning technologies.

Key Responsibilities

• Design, build, and maintain automated infrastructure provisioning pipelines using PXE, iPXE, and Zero Touch Provisioning (ZTP).

• Manage and optimize Kubernetes clusters supporting large-scale GPU and AI inference workloads.

• Perform advanced Linux systems administration, kernel tuning, and performance optimization.

• Troubleshoot Linux kernel, systemd, NUMA, PCIe, and storage I/O performance issues.

• Manage GPU infrastructure, including NVIDIA drivers, CUDA, NCCL, and fabric managers.

• Develop infrastructure automation using Python or Go.

• Implement configuration management and infrastructure as code using Terraform, Ansible, and SaltStack.

• Establish and maintain SLIs, SLOs, monitoring, and observability frameworks.

• Build automated self-healing mechanisms to improve infrastructure reliability and availability.

• Troubleshoot distributed systems, hardware, networking, and GPU performance bottlenecks.

• Drive root cause analysis (RCA) and implement permanent solutions to recurring infrastructure issues.

• Improve operational efficiency through automation and reduction of manual intervention.

• Support the lifecycle management of large-scale compute and accelerator infrastructure.

Required Qualifications

• Strong experience as a Systems Engineer, Site Reliability Engineer, or Infrastructure Engineer.

• Extensive hands-on experience with Linux internals, including kernel tuning, systemd, and performance troubleshooting.

• Strong experience managing Kubernetes clusters in production environments.

• Experience with bare-metal provisioning technologies such as PXE, iPXE, and ZTP.

• Hands-on experience with NVIDIA GPU infrastructure, drivers, CUDA, and NCCL.

• Strong understanding of NUMA architectures, PCIe subsystems, and storage I/O optimization.

• Proficiency in Python or Go for infrastructure automation and tooling.

• Experience with Terraform, Ansible, or SaltStack.

• Knowledge of monitoring, telemetry, SLIs, SLOs, and infrastructure observability.

• Experience supporting large-scale distributed systems and hyperscale infrastructure.

• Strong troubleshooting, debugging, and root cause analysis skills.

Preferred Qualifications

• Experience working with GPU cloud providers, AI infrastructure companies, or hyperscale cloud environments.

• Previous experience at companies such as CoreWeave, Nebius, Crusoe Cloud, AWS, or Google Cloud.

• Experience with high-performance computing (HPC) and distributed GPU clusters.

• Familiarity with Prometheus, Grafana, VictoriaMetrics, and OpenTelemetry.

• Understanding of high-performance networking technologies, including RoCEv2 and InfiniBand.

• Experience with automated infrastructure recovery and self-healing systems.

• Experience operating infrastructure across thousands of bare-metal servers or accelerator nodes.

Ideal Candidate Profile

We are looking for a hands-on infrastructure engineer with deep Linux systems expertise and strong SRE fundamentals. The ideal candidate should understand how bare-metal hardware, GPU accelerators, operating systems, and Kubernetes work together to support large-scale AI inference workloads.

Candidates with experience operating GPU clusters, hyperscale compute infrastructure, and highly automated data center environments will be strongly preferred.