Senior Platform Engineer

Job summary

While technology is the heart of our business, a global and diverse culture is the heart of our success. We love our people and we take pride in catering them to a culture built on transparency, diversity, integrity, learning and growth.

Qualifications

• Experience Level: 10+ yrs
• This role is ideal for someone with deep hands-on experience in GPU profiling, distributed training, and high-performance compute environments.
• Strong experience with Slurm and distributed training environments
• Hands-on expertise with Red Hat OpenShift and/or Kubernetes
• Deep knowledge of the NVIDIA GPU ecosystem (CUDA, cuDNN, NCCL, Nsight, Triton/TensorRT)
• Strong foundation in Linux systems, performance tuning, and multi-GPU optimization
• Experience deploying GenAI workloads (LLM fine-tuning, RAG pipelines, multi-modal systems)
• Familiarity with Infrastructure-as-Code
• OQs):
• Experience with NVIDIA NIMs, DGX systems, or GPU-accelerated containers
• Knowledge of LLMOps frameworks and MLOps integration
• Familiarity with vector databases and retrieval systems for RAG architectures

Responsibilities

• Design and implement scalable infrastructure for LLM and GenAI workloads across multi-GPU environments
• Perform GPU profiling, benchmarking, and performance optimization for distributed training workloads
• Manage and schedule compute-intensive jobs using Slurm-based clusters and OpenShift/Kubernetes environments
• Enable and optimize the NVIDIA GPU stack (CUDA, cuDNN, NCCL, Triton, RAPIDS, etc.)
• Collaborate with cross-functional teams to deploy models in research and production environments
• Build and support GenAI pipelines (fine-tuning, RAG, multi-modal inferencing, LLMOps)
• Develop reusable infrastructure templates using

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