Nvidia GPU Platform Engineer

Experience: 512 Years
Location: Mumbai, Pune, Bangalore, Hyderabad, Chennai, Noida
Employment Type: Full-time

Job Summary

We are looking for an experienced NVIDIA GPU Platform Engineer to build, operate, and optimize GPU-accelerated AI/ML infrastructure on Kubernetes and AWS. The role focuses on NVIDIA GPU platforms, LLM inference/model serving, GPU scheduling and partitioning, autoscaling, telemetry, and performance optimization.

Key Responsibilities

• Design and manage NVIDIA GPU platforms for AI/ML workloads.
• Deploy and manage CUDA, NVIDIA GPU Operator and DCGM.
• Build and operate Kubernetes-based GPU workloads using GPU Device Plugins.
• Implement GPU scheduling using Run or equivalent technologies.
• Configure MIG and fractional GPU capabilities for efficient GPU utilization.
• Deploy and optimize model-serving platforms including NVIDIA NIM, Dynamo, Triton, TensorRT-LLM and vLLM.
• Implement KEDA-based autoscaling for inference workloads.
• Develop and integrate OpenAI-compatible inference APIs.
• Implement GPU telemetry, monitoring, utilization and performance dashboards.
• Support LLM and RAG inference workloads, including vector database integrations.
• Perform inference benchmarking, capacity planning, performance tuning and optimization.
• Work with NVIDIA AI Enterprise (NVAIE) on AWS/EKS and GPU-enabled cloud/edge platforms.
• Support AWS Outposts G7 / Edge GPU platforms where required.

Mandatory Skills

• Strong hands-on experience with NVIDIA GPUs and CUDA
• NVIDIA GPU Operator, DCGM
• Kubernetes GPU workloads and Device Plugins
• Model serving: NIM / Triton / TensorRT-LLM / vLLM / Dynamo
• GPU scheduling Run or equivalent
• MIG / Fractional GPU
• KEDA autoscaling
• OpenAI-compatible inference APIs
• GPU telemetry, monitoring and performance optimization

Good-to-Have Skills

• NVAIE on AWS/EKS
• AWS Outposts / Edge GPU platforms
• LLM and RAG serving
• Vector databases
• GPU/inference performance benchmarking
• TensorRT and CUDA performance tuning
• AI/ML platform engineering experience

Ideal Candidate

Candidates should have strong hands-on experience in GPU infrastructure + Kubernetes + AI model serving, rather than purely application-level ML development. Experience deploying and optimizing production-grade GPU inference platforms will be highly preferred.