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Quantiphi

Tech Architect

Quantiphi

. Design and implement scalable infrastructure for LLM and GenAI workloads across multi-GPU environments .

Posted 10/15/2026full-timeRemote • United States, CanadaSeniorLeadWebsite

Core Competencies

Role fit
Core Competencies

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Demonstrates expertise in designing and implementing scalable infrastructure for LLM and GenAI workloads, with a strong focus on performance optimization and multi-GPU environments. Proficient in managing compute-intensive jobs and deploying models in both research and production settings.

Highest-signal resume keywords
10+ Years of ExperienceSlurm and Distributed Training EnvironmentsNVIDIA GPU Ecosystem (CUDA, cuDNN, NCCL)Red Hat OpenShift and/or KubernetesInfrastructure-as-Code (Terraform, Ansible)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills
GPU ProfilingPerformance OptimizationMulti-GPU OptimizationGenAI Workloads DeploymentLLM Fine-TuningRAG PipelinesCloud GPU Environments (GCP, Azure, AWS, OCI)NVIDIA NIMsDGX SystemsVector Databases
Soft Skills
CollaborationClient-Facing Engagements
Tools & Technologies
SlurmOpenShiftKubernetesTerraformHelmNVIDIA TritonNVIDIA NsightNVIDIA RAPIDSMLOps IntegrationClinical Decision Support Systems
Industry Keywords
Healthcare Domain ExperienceFHIR R4HL7 v2SMART on FHIREHR IntegrationHIPAA ComplianceClinical WorkflowsCDS HooksHealthcare Interoperability Solutions

Tech Stack

Tools & technologies
AnsibleAWSAzureCloudGoogle Cloud PlatformKubernetesLinuxOpenShiftTerraform

About the role

Key responsibilities & impact
  • 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, including CUDA, cuDNN, NCCL, Triton, and RAPIDS
  • Collaborate with cross-functional teams to deploy models in research and production environments
  • Build and support GenAI pipelines, including fine-tuning, RAG, multi-modal inferencing, and LLMOps
  • Develop reusable infrastructure templates using Terraform and Helm
  • Contribute to internal innovation through proofs of concept and workshops
  • Support client-facing delivery engagements
  • Partner with data science, MLOps, and application teams to build production-grade GenAI platform foundations

Requirements

What you’ll need
  • 10+ years of experience
  • 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, including CUDA, cuDNN, NCCL, Nsight, Triton/TensorRT
  • Strong foundation in Linux systems, performance tuning, and multi-GPU optimization
  • Experience deploying GenAI workloads, including LLM fine-tuning, RAG pipelines, and multi-modal systems
  • Familiarity with Infrastructure-as-Code tools such as Terraform and Ansible
  • Experience with cloud GPU environments, including GCP, Azure, AWS, OCI, and/or on-prem GPU clusters
  • 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 is nice to have: FHIR R4, HL7 v2, SMART on FHIR, EHR integration, HIPAA compliance, clinical workflows, CDS Hooks, patient-facing applications, clinical decision support systems, or healthcare interoperability solutions

Benefits

Comp & perks
  • Upskilling and professional development opportunities
  • Exposure to cutting-edge AI, ML, data, and cloud technologies
  • Opportunity to work with Fortune 500 companies
  • Work in a research-focused organization with 60+ patents filed
  • Collaboration with passionate, talented colleagues
  • Career growth in a fast-growing AI-first digital engineering company