Senior Platform Engineer

Quantiphi

Senior Platform Engineer designing GPU infrastructure and GenAI platforms for Quantiphi, an AI-first digital engineering company. Optimizing distributed training, LLMOps, and production AI deployments across cloud and on-premises environments.

Posted 8/16/2026full-timeRemote • Massachusetts, New Jersey • 🇺🇸 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 deployment in multi-GPU environments. Proficient in managing compute-intensive jobs and utilizing Infrastructure-as-Code tools to enhance operational efficiency.

Highest-signal resume keywords

Slurm ManagementNVIDIA GPU EcosystemOpenShift/Kubernetes ExpertiseInfrastructure-as-Code (Terraform)GenAI Workload Deployment

ATS Keywords

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Hard Skills

CUDACuDNNNCCLTritonRAPIDSLinux SystemsPerformance TuningMulti-GPU OptimizationLLM Fine-TuningRAG Pipelines

Soft Skills

CollaborationClient-Facing Communication

Tools & Technologies

SlurmOpenShiftKubernetesTerraformAnsibleGCPAzureAWSOCINVIDIA NIMs

Industry Keywords

Healthcare Domain ExperienceFHIR R4HL7 v2SMART on FHIREHR IntegrationsHIPAACDS HooksClinical WorkflowsClinical Decision Support Systems

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, multimodal 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

Requirements

What you’ll need

5+ 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 multimodal 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-premises 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, including FHIR R4, HL7 v2, SMART on FHIR, EHR integrations, HIPAA, CDS Hooks, clinical workflows, and clinical decision support systems

Benefits

Comp & perks

Upskilling and professional development opportunities
Exposure to AI, ML, data, and cloud technologies
Opportunity to work with Fortune 500 companies
Research-focused organization with 60+ patents filed
Collaboration with talented colleagues around the globe
Hybrid work culture