Senior Platform Engineer

About the role

We are looking for a highly skilled Senior 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.

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

Healthcare Domain Experience (Nice To Have)

• Experience working with FHIR R4, HL7 v2, or SMART on FHIR
• Integration with EHR systems (e.g., Epic)
• Understanding of HIPAA compliance and healthcare data privacy
• Exposure to clinical workflows, CDS Hooks, or patient-facing applications
• Experience building clinical decision support systems or healthcare interoperability solutions