Architect - Platform Engineer

About the role

We are seeking a highly skilled Architect - Platform Engineer to design, optimize, and scale infrastructure for Large Language Models (LLM) and Generative AI (GenAI) workloads. This role requires deep hands-on experience in GPU profiling, distributed training, and high-performance compute environments.

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 (Proof of Concepts, workshops) and support client-facing delivery engagements
• Develop and deliver automation software required for building & improving the functionality, reliability, availability, and manageability of applications and cloud platforms
• Champion and drive the adoption of Infrastructure as Code (IaC) practices and mindset
• Design, architect, and build self-service, self-healing, synthetic monitoring and alerting platform and tools
• Automate the development and test automation processes through CI/CD pipeline (Git, Jenkins, SonarQube, Artifactory, Docker containers)
• Build container hosting-platform using Kubernetes
• Introduce new cloud technologies, tools; processes to keep innovating in the commerce area to drive greater business value
• Lead the technical discussion regarding architecture designing and troubleshooting with the clients and provide solutions proactively as required

Requirements

• 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
• Serve as a mentor or guide for senior resources / team leads
• Lead the technical discussion regarding architecture design

Qualifications

• 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