Quantiphi, an AI-first digital engineering and consulting company, is seeking a highly skilled Senior Platform Engineer to design, optimize, and scale infrastructure for GenAI and large language model workloads. This role focuses on GPU profiling, distributed training, and high-performance compute environments, and offers the opportunity to build GenAI platform foundations and support production deployments in collaboration with data science, MLOps, and application teams. 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; Manage compute jobs using Slurm clusters and OpenShift/Kubernetes; Enable and optimize the NVIDIA GPU stack (CUDA, cuDNN, NCCL, Triton, RAPIDS); Collaborate with cross-functional teams to deploy models in research and production; Build and support GenAI pipelines (fine-tuning, RAG, multi-modal inferencing, LLMOps); Develop reusable infrastructure templates using Terraform and Helm; Contribute to internal PoCs/workshops and support client-facing delivery. Qualifications: Strong experience with Slurm and distributed training; Hands-on experience with Red Hat OpenShift and/or Kubernetes; Deep knowledge of NVIDIA GPU ecosystem; Linux systems, performance tuning, and multi-GPU optimization; Experience deploying GenAI workloads (LLM tuning, RAG, multi-modal); Infra-as-code tools (Terraform, Ansible); Cloud GPU environments (GCP, Azure, AWS, OCI) and/or on-prem GPU clusters; Nice-to-have: NVIDIA NIMs/DG

Quantiphi, an AI-first digital engineering and consulting company, is seeking a highly skilled Senior Platform Engineer to design, optimize, and scale infrastructure for GenAI and large language model workloads. This role focuses on GPU profiling, distributed training, and high-performance compute environments, and offers the opportunity to build GenAI platform foundations and support production deployments in collaboration with data science, MLOps, and application teams. 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; Manage compute jobs using Slurm clusters and OpenShift/Kubernetes; Enable and optimize the NVIDIA GPU stack (CUDA, cuDNN, NCCL, Triton, RAPIDS); Collaborate with cross-functional teams to deploy models in research and production; Build and support GenAI pipelines (fine-tuning, RAG, multi-modal inferencing, LLMOps); Develop reusable infrastructure templates using Terraform and Helm; Contribute to internal PoCs/workshops and support client-facing delivery. Qualifications: Strong experience with Slurm and distributed training; Hands-on experience with Red Hat OpenShift and/or Kubernetes; Deep knowledge of NVIDIA GPU ecosystem; Linux systems, performance tuning, and multi-GPU optimization; Experience deploying GenAI workloads (LLM tuning, RAG, multi-modal); Infra-as-code tools (Terraform, Ansible); Cloud GPU environments (GCP, Azure, AWS, OCI) and/or on-prem GPU clusters; Nice-to-have: NVIDIA NIMs/DG