Senior Applied AI Infrastructure Engineer - NREC

Carnegie Mellon University•Pittsburgh, PA

75d

About The Position

The National Robotics Engineering Center (NREC) seeks a Senior Applied AI Infrastructure Engineer to evaluate, deploy, and integrate secure generative and agentic AI tools, large language models, and self-hosted infrastructure across engineering workflows. The role advances internal AI-assisted workflows, infrastructure reliability, and secure multi-GPU model serving. It includes supporting cloud-hosted tools where appropriate and locally hosted tools when project confidentiality or data-handling requirements rule out cloud use. The engineer designs, implements, documents, tests, and maintains internally hosted AI services and supporting infrastructure; operates LLMs on shared GPU systems and smaller project- or team-specific platforms; and integrates AI tools with engineering systems. The position also develops secure interfaces and sandboxed environments for AI agents, prototypes and evaluates AI-assisted engineering workflows, and supports engineers across software, embedded, FPGA, mechanical, electrical, and other technical disciplines. The role creates documentation, examples, training materials, and reusable configurations, evaluates workflow reliability and usefulness, surveys emerging tools, and follows established software-development and security practices. NREC is part of Carnegie Mellon University's Robotics Institute and is based in Pittsburgh, Pennsylvania. The organization describes a collaborative, flexible environment and opportunities to develop expertise, lead projects, mentor others, and contribute to robotics projects in areas including agriculture, manufacturing, defense, and energy. This position may involve projects with military or defense applications or sponsors and requires a successful pre-employment background check.

Requirements

Bachelor of Science in Computer Science, Computer Engineering, Electrical Engineering, or a related technical discipline, or equivalent experience.
At least 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 in at least 3 of the following areas: deploying and maintaining software services; containers and reproducible deployment tools such as Docker; integrating software systems through APIs, command-line tools, authentication mechanisms, or similar interfaces; modern software engineering practices including version control, code review, testing, CI/CD, logging, and troubleshooting; evaluating new technologies, communicating technical tradeoffs, and making practical recommendations.
Successful pre-employment background check.
The position involves a variety of projects, including projects involving military/defense applications and/or funded by military/defense sponsors.

Nice To Haves

Experience with self-hosted LLM inference frameworks such as vLLM, TensorRT-LLM, llama.cpp, Ollama, NVIDIA NIM, or similar tools.
Experience with multi-GPU systems, model serving, resource scheduling, or inference performance optimization.
Experience with Model Context Protocol servers or other structured interfaces for AI tool use.
Experience integrating with Jira, Confluence, Jenkins, Git-based repositories, artifact repositories, or internal knowledge systems.
Experience with coding agents that modify code, run builds and tests, and prepare pull requests.
Experience with sandboxed code execution, container isolation, secrets management, access control, or audit logging.
Experience evaluating LLM applications, coding assistants, agents, or retrieval systems.
Experience with retrieval-augmented generation, document ingestion, embeddings, reranking, or code indexing.
Experience with cloud AI services and data-sensitive or disconnected AI deployments.
Experience with embedded software, FPGA development, robotics, simulation, or hardware-in-the-loop testing.
Experience with GPU-based machine-learning infrastructure.
Experience developing internal technical documentation, training, examples, or reusable engineering workflows.
Experience with machine learning, computer vision, or robotics applications.

Responsibilities

Evaluate generative and agentic AI tools and recommend practical approaches to engineering leadership.
Support cloud-hosted AI tools where appropriate and locally hosted tools when confidentiality or data-handling requirements prohibit cloud use.
Design, implement, document, test, and maintain internally hosted AI services and supporting infrastructure.
Deploy and operate large language models on shared GPU systems and smaller project- or team-specific platforms.
Integrate AI tools with engineering systems, including source-code repositories, Jira, Confluence, Jenkins, internal documentation, and test infrastructure.
Develop secure tool interfaces, APIs, Model Context Protocol servers, and sandboxed environments for AI agents to perform engineering tasks.
Prototype and evaluate AI-assisted workflows for software development, testing, documentation, requirements analysis, and other engineering activities.
Help engineers use supported AI tools across software, embedded, FPGA, mechanical, electrical, and other technical workflows.
Develop internal documentation, examples, training materials, and reusable configurations for recommended tools and practices.
Measure the reliability and usefulness of AI-assisted workflows, including generated-code quality, test results, review effort, and failure modes.
Survey emerging tools and techniques and implement promising approaches when they provide practical value.
Follow team software-development best practices, including peer review, automated testing, version control, issue tracking, security review, and integrated documentation.