Platform Engineer

Role: Architect - Platform Engineer Experience Level: 10+ yrs Work Location: 100% Remote -East/Canada [ET & CT] Role Overview: We are looking for a highly skilled Architect - Platform Engineer to design, optimize, and scale infrastructure for GenAI and LLM workloads. This role is ideal for someone with deep hands-on experience in GPU profiling, distributed training, and high-performance compute environments. You’ll play a key role in building out GenAI platform foundations, supporting production-grade deployments, and partnering closely with data science, MLOps, and application teams to bring cutting-edge AI solutions to life. Key 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 tools like Terraform and Helm ● Contribute to internal innovation (PoCs, workshops) and support client-facing delivery engagements Basic Qualifications: ● 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 Other Qualifications (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 ● Comfortable working in client-facing environments and collaborating with AI solution teams Pay: $140,000.00-$150,000.00 per year Experience: • NVIDIA GPU: 1 year (required) • AI/ML: 1 year (required) Work Location: Remote