Senior Platform Engineer

Role Description

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.

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.

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.

Benefits

• Make an impact at one of the world’s fastest-growing AI-first digital engineering companies.
• Upskill 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!