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 tools like Terraform and Helm.

10+ years of experience in relevant field.
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 tools (Terraform, Ansible).
Experience with cloud GPU environments (GCP, Azure, AWS, OCI) and/or on-prem GPU clusters.

Up-skill and discover your potential as you solve complex challenges in cutting-edge areas of technology alongside passionate, talented colleagues.
Work where innovation happens
work with disruptive innovators in a research-focused organization with 60+ patents filed across various disciplines.
Stay ahead of the curve, immerse yourself in breakthrough AI, ML, data, and cloud technologies and gain exposure working with Fortune 500 companies.
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us !

Architect – Platform Engineer

Key skills