GPU Infrastructure Specialist

Dear all,

We are looking for a GPU Infrastructure Specialist to manage and optimize GPU-based environments for model hosting and high-performance computing workloads. The ideal candidate will have hands-on experience with NVIDIA/ AMD, SambaNova GPU ecosystems, and a strong background in resource management, performance tuning, and observability within large-scale AI/ML environments.

Responsibilities

• Manage, configure, and maintain GPU infrastructure across on-premise and cloud environments.
• Handle GPU resource allocation, scheduling, and orchestration for AI/ML workloads.
• Oversee driver updates, operator management, and compatibility across multiple GPU vendors (NVIDIA, AMD, SambaNova).
• Implement GPU tuning and performance optimization strategies to ensure efficient model inference and training performance.
• Monitor GPU utilization, latency, and system health using observability and alerting tools (e.g., Prometheus, Grafana, NVIDIA DCGM, etc.).
• Collaborate with AI engineers, DevOps, and MLOps teams to ensure seamless model deployment and hosting across GPU clusters.
• Develop automation scripts and workflows for GPU provisioning, scaling, and lifecycle management.
• Troubleshoot GPU performance issues, memory bottlenecks, and hardware-level anomalies.

Qualifications

• Strong experience managing GPU infrastructure (NVIDIA, AMD, SambaNova).
• Proficiency in resource scheduling and orchestration (Kubernetes, Slurm, Ray, or similar).
• Knowledge of driver and operator management in multi-vendor environments.
• Experience with GPU tuning, profiling, and performance benchmarking.
• Familiarity with observability and alerting tools (Prometheus, Grafana, ELK Stack, etc.).
• Hands-on experience with model hosting platforms (Triton Inference Server, TensorRT, ONNX Runtime, etc.) is a plus.
• Working knowledge of Linux systems, Docker/Kubernetes, and CI/CD pipelines.
• Strong scripting skills in Python, Bash, or Go.

Preferred Skills

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
• Certifications in GPU computing (e.g., NVIDIA Certified Administrator, CUDA, or similar).
• Experience with AI/ML model lifecycle management in production environments.