Platform Engineer

Key Responsibilities:Design and implement scalable infrastructure for LLM and GenAI workloads across multi-GPU environmentsPerform GPU profiling, benchmarking, and performance optimization for distributed training workloadsManage and schedule compute-intensive jobs using Slurm-based clusters and OpenShift/Kubernetes environmentsEnable and optimize the NVIDIA GPU stack (CUDA, cuDNN, NCCL, Triton, RAPIDS, etc.)Collaborate with cross-functional teams to deploy models in research and production environmentsBuild and support GenAI pipelines (fine-tuning, RAG, multi-modal inferencing, LLMOps)Develop reusable infrastructure templates using tools like Terraform and HelmContribute to internal innovation (PoCs, workshops) and support client-facing delivery engagementsBasic Qualifications:Strong experience with Slurm and distributed training environmentsHands-on expertise with Red Hat OpenShift and/or KubernetesDeep knowledge of the NVIDIA GPU ecosystem (CUDA, cuDNN, NCCL, Nsight, Triton/TensorRT)Strong foundation in Linux systems, performance tuning, and multi-GPU optimizationExperience 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 clustersOther Qualifications (OQs):Experience with NVIDIA NIMs, DGX systems, or GPU-accelerated containersKnowledge of LLMOps frameworks and MLOps integrationFamiliarity with vector databases and retrieval systems for RAG architecturesComfortable working in client-facing environments and collaborating with AI solution teamsHealthcare Domain Experience (Nice to Have):Experience working with FHIR R4, HL7 v2, or SMART on FHIRIntegration with EHR systems (e.g., Epic)Understanding of HIPAA compliance and healthcare data privacyExposure to clinical workflows, CDS Hooks, or patient-facing applicationsExperience building clinical decision support systems or healthcare interoperability solutions