Senior AI Platform Engineer

University of the Cumberlands

. Design, deploy, and manage on-premises and hybrid AI infrastructure, including NVIDIA GPU environments and NVIDIA AI Enterprise .

Posted 11/12/2026full-timeRemote • Kentucky • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in deploying and managing AI infrastructure, particularly with NVIDIA GPU environments and AI Enterprise. Proficient in platform engineering, DevOps practices, and optimizing AI/ML environments for performance and security.

Highest-signal resume keywords

NVIDIA GPU Infrastructure ManagementPlatform EngineeringAI/ML Environment DeploymentScripting with Python or BashAPI Development and Security

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

NVIDIA AI EnterpriseNVIDIA NIMCUDA OptimizationLinux AdministrationContainerizationInfrastructure as CodeMLOps/LLMOpsVLLMOllamaHugging Face

Tools & Technologies

DockerKubernetesCI/CDHybrid CloudAPI Gateways

Industry Keywords

Higher EducationResearch ComputingVector DatabasesEmbeddingsRetrieval-Augmented Generation

Tech Stack

Tools & technologies

CloudDockerKubernetesLinuxPython

About the role

Key responsibilities & impact

Design, deploy, and manage on-premises and hybrid AI infrastructure, including NVIDIA GPU environments and NVIDIA AI Enterprise
Deploy, optimize, and maintain locally hosted LLMs and inference services using NVIDIA NIM, vLLM, Ollama, or comparable tools
Build secure APIs and services connecting approved AI capabilities to University applications and users
Monitor performance, optimize GPU resources, troubleshoot issues, and maintain security, reliability, and scalability
Use Linux, containers, scripting, and platform engineering practices to streamline operations
Partner across IT and the AI Lab, evaluate emerging technologies, and influence platform architecture

Requirements

What you’ll need

Significant professional experience in platform engineering, DevOps, systems engineering, or enterprise infrastructure
Demonstrated responsibility for deploying and supporting production AI/ML environments
Hands-on experience deploying or administering NVIDIA GPU-based AI infrastructure, NVIDIA AI Enterprise, NVIDIA NIM, or comparable AI/ML and enterprise inference platforms
Strong Linux, networking, containers, and modern infrastructure knowledge
Scripting and automation experience with Python, Bash, or similar tools
Familiarity with APIs, authentication, security, and monitoring
Bachelor’s degree in a related technical field or equivalent professional experience
Direct experience with NVIDIA AI Enterprise and NVIDIA NIM; CUDA or GPU performance optimization
Experience with vLLM, Ollama, Hugging Face, Docker, Kubernetes, infrastructure as code, or CI/CD
Knowledge of MLOps/LLMOps, vector databases, embeddings, or retrieval-augmented generation (RAG)
Experience with enterprise API gateways, hybrid cloud, higher education, or research computing

Benefits

Comp & perks

Opportunity to build and shape a new enterprise AI platform
Hands-on work with NVIDIA GPU computing, enterprise AI software, and open-weight language models
Opportunity to support teaching, learning, research, and institutional innovation
Influence over tool selection, technical standards, and platform improvements