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About Carnegie Mellon University
Carnegie Mellon University is a leading private research university in Pittsburgh, Pennsylvania, internationally recognized for programs in computer science, engineering, business, the arts, and artificial intelligence. Industry: Higher Education & Research
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The Senior Applied AI Infrastructure Engineer joins the National Robotics Engineering Center to lead the evaluation, deployment, and integration of secure generative and agentic AI tools. You will design, implement, and maintain internally hosted AI services and support infrastructure, specifically focusing on large language models on shared GPU systems. Key responsibilities include integrating AI tools with engineering systems like Jira, Confluence, and Jenkins, while developing secure APIs, Model Context Protocol servers, and sandboxed environments. You will utilize Python, Linux, Docker, and frameworks such as vLLM, TensorRT-LLM, and llama.cpp. The role addresses the technical challenge of providing secure, multi-GPU model serving and AI-assisted workflows for software, embedded, FPGA, and mechanical engineering tasks within industrial, commercial, and government sectors.
Evaluate generative and agentic AI tools and recommend practical approaches to engineering leadership.
Design, implement, document, test, and maintain internally hosted AI services and supporting infrastructure.
Deploy and operate large language models on shared GPU systems and team-specific platforms.
Integrate AI tools with engineering systems like source-code repositories, Jira, Confluence, and Jenkins.
Develop secure tool interfaces, APIs, Model Context Protocol servers, and sandboxed environments for AI agents.
Prototype and evaluate AI-assisted workflows for software development, testing, documentation, and requirements analysis.
Measure the reliability and usefulness of AI-assisted workflows, including code quality and failure modes.
Create internal documentation, training materials, and reusable configurations for recommended AI tools and practices.
What we're looking for
B.S. in Computer Science, Computer Engineering, Electrical Engineering, or a related technical discipline, or equivalent experience.
5+ years of professional software engineering, machine-learning infrastructure, DevOps, platform engineering, or developer-tools experience.
Strong Python programming skills.
Linux development and system-administration experience.
Familiarity with large language models, retrieval-augmented generation, tool-using agents, or AI-assisted software-development workflows.
Strong technical communication and documentation skills.
Experience with at least three of the following: deploying software services, Docker/containers, API integration, or modern software engineering practices.
OCI
GPU
RoCE
Infiniband
Distributed Systems
Linux
Python
Java
C
C++
GoLang
Shell Scripting
SQL
MySQL
Redis
Memcache
Infrastructure as Code
Telemetry
Load Testing
AI
Agentic AI
Cloud Infrastructure
CI/CD
SRE
Distributed Systems
Data Modeling
Mainframe
Midrange
Observability
SDLC
Automation
Networking
Storage Engineering