Senior Applied AI Infrastructure Engineer - NREC

About the role

The National Robotics Engineering Center (NREC) seeks a Senior Applied AI Infrastructure Engineer to lead and contribute to the evaluation, deployment, and integration of secure generative and agentic AI tools, Large Language Models (LLMs), and support of self-hosted infrastructure across engineering workflows. This role involves advancing internal AI-assisted workflows, infrastructure reliability, and secure multi-GPU model serving.

Responsibilities

• Evaluating generative and agentic AI tools and recommending practical approaches to engineering leadership.
• Supporting cloud-hosted AI tools where appropriate and locally hosted tools where project confidentiality or data-handling requirements prohibit cloud use.
• Designing, implementing, documenting, testing, and maintaining internally hosted AI services and supporting infrastructure.
• Deploying and operating large language models on shared GPU systems and smaller project- or team-specific platforms.
• Integrating AI tools with engineering systems such as source-code repositories, Jira, Confluence, Jenkins, internal documentation, and test infrastructure.
• Developing secure tool interfaces, APIs, Model Context Protocol servers, and sandboxed environments that allow AI agents to perform useful engineering tasks.
• Prototyping and evaluating AI-assisted workflows for software development, testing, documentation, requirements analysis, and other engineering activities.
• Helping engineers use supported AI tools effectively across software, embedded, FPGA, mechanical, electrical, and other technical workflows.
• Developing internal documentation, examples, training materials, and reusable configurations for recommended tools and practices.
• Making measurements of the reliability and usefulness of AI-assisted workflows, including the quality of generated code, test results, review effort, and failure modes.
• Surveying emerging tools and techniques and implementing promising approaches where they provide practical value.
• Following best practices for team software development, including peer review, automated testing, version control, issue tracking, security review, and integrated documentation.

Requirements

• Bachelor's degree 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.
• 3 or more of the following: Experience deploying and maintaining software services, experience with containers and reproducible deployment tools such as Docker, experience integrating software systems through APIs, command-line tools, authentication mechanisms, or similar interfaces, experience with modern software engineering practices, including version control, code review, testing, CI/CD, logging, and troubleshooting.
• Ability to evaluate new technologies, communicate technical tradeoffs, and make practical recommendations.

Other Requirements

• Successful pre-employment background check.
• Sponsorship: Applicants for this position must be currently legally authorized to work for CMU in the United States. CMU will not sponsor or take over the sponsorship of an employment visa for this opportunity. Carnegie Mellon is not a qualifying employer for the STEM OPT benefit: only the 12-month OPT may be used to work at Carnegie Mellon.
• Travel Requirements: You should be able to travel domestically and be willing to work outside of normal business hours as needed.