Senior Tech Architect – PE

Quantiphi

Platform Architect scaling GPU infrastructure for GenAI and LLM workloads at Quantiphi, an AI-first digital engineering company. Optimizing distributed training, Kubernetes clusters, and production AI deployments.

Posted 8/16/2026full-timeRemote • 🇺🇸 United StatesSeniorWebsite

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

Slurm ExperienceNVIDIA GPU Ecosystem KnowledgeRed Hat OpenShift ExpertiseInfrastructure-as-Code ProficiencyGenAI Workload Deployment

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

CUDACuDNNNCCLTritonRAPIDSLinux SystemsPerformance TuningMulti-GPU OptimizationLLM Fine-TuningRAG Pipelines

Soft Skills

CollaborationClient-Facing Communication

Tools & Technologies

TerraformHelmOpenShiftKubernetesGCPAzureAWSOCINVIDIA NIMsDGX Systems

Industry Keywords

Healthcare Domain ExperienceFHIR R4HL7 v2SMART on FHIREHR SystemsHIPAA ComplianceClinical WorkflowsCDS HooksPatient-Facing ApplicationsHealthcare 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 PoCs and workshops
Support client-facing delivery engagements

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 systems such as Epic, HIPAA compliance, clinical workflows, CDS Hooks, patient-facing applications, clinical decision support systems, or healthcare interoperability solutions

Benefits

Comp & perks

Upskill and discover your potential through complex challenges in cutting-edge technology
Work in a research-focused organization with 60+ patents filed
Exposure to AI, ML, data, and cloud technologies
Gain exposure working with Fortune 500 companies
Opportunities to learn, grow, and interact with colleagues from varied experience and backgrounds around the globe
Diverse and hybrid work culture