Architect Platform Engineer

Work Location

• US East/Canada (Remote)

Role Overview

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
• 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

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

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)

What’s in it for YOU at Quantiphi