Role description

NVIDIA GPU Platform Engineer

Role Summary The NVIDIA GPU Platform Engineer DevOps builds and operates the GPU and inferenceserving stack NVIDIA AI Enterprise on EKS NIM microservices and NVIDIA Dynamo serving and owns GPU sharing scheduling integration and serving performance Onshore placement supports handson GPUedge coordination and close work with the architecture team

Key Responsibilities

Deploy and operate NVIDIA AI Enterprise NVAIE on EKS GPU Operator drivers CUDA runtime and DCGM

Deploy and operate NIM microservices and NVIDIA Dynamo serving exposing the OpenAIcompatible API chatcompletions embeddings streaming batch

Configure GPU sharing MIG where the SKU supports it and RunAI fractional GPU as the isolation baseline and clearly distinguish hardware vs software isolation

Integrate RunAI for GPU scheduling quota and preemption and KEDA for replica autoscaling including scaletozero





Instrument GPU telemetry via DCGM and drive inference performance validation and SLAtier binding

Support the modelonboarding NIM packaging and inferencedeploy pipelines

Tune GPUserving performance and troubleshoot CUDA driver and scheduling issues

MustHave Skills Experience

8 years in infrastructureML engineering with handson NVIDIA GPU operations

NVIDIA GPU stack drivers CUDA GPU Operator and DCGM

DevOpd Experience

Model serving Triton NVIDIA Dynamo NIM TensorRTLLM andor vLLM

Kubernetes GPU workloads and device plugins

GPU scheduling RunAI or equivalent and GPU partitioning MIG fractional GPU

Autoscaling KEDA and OpenAIcompatible inference API patterns

NicetoHave Skills

NVAIE on AWSEKS and edge GPU Outposts G7

LLM RAG serving and vector databases

Inference performance benchmarking and optimization

📌 Specialist - Cloud Engineering (Chennai)
🏢 LTM
📍 Chennai