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the deployment methods for GPU-accelerated serving frameworks in the market, with reference implementations and best-practice recommendations for large-scale serving solutions (eg, NVIDIA Triton Inference Server, TensorRT, ONNX Runtime). Develop repeatable and automated configuration templates for GPU resources. Implement active GPU monitoring, including review and analysis of all relevant metrics (utilization, memory bandwidth, power, temperature, etc.), and … NVIDIA Certified (Preferred). Required Skills Direct experience with GPU services, including resource provisioning, scaling, and optimization. Demonstrable expertise in GPU-accelerated software development (CUDA, OpenCL, TensorRT, PyTorch, TensorFlow, ONNX, etc.). Strong background in performance benchmarking, profiling (Nsight, nvprof, or similar tools), and workload tuning. Experience with Infrastructure as Code (Terraform, HELM Charts, or equivalent) for automated cloud resource More ❯