Senior AI Infrastructure Engineer – Secure LLMs & Multi-GPU

Carnegie-Mellon-University; Pittsburgh (Allegheny County); On-site; USD 120,000 - 160,000; Full time

Benefits offered by this job

Comprehensive medical, prescription,,d

Tuition benefits

Generous retirement savings program

Job summary

Carnegie Mellon University’s National Robotics Engineering Center (NREC) seeks a Senior Applied AI Infrastructure Engineer to lead evaluation, deployment, and integration of secure generative AI tools and self-hosted AI services across engineering workflows. You will enable AI-assisted workflows, ensure infrastructure reliability, and support multi-GPU model serving.

You will collaborate with leadership and multidisciplinary teams to ship practical AI infrastructure that powers robotics

Qualifications

• B.S. in Computer Science, Computer Engineering, Electrical Engineering, or related technical discipline, or equivalent experience.
• 5+ years of professional software engineering or ML infrastructure 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 workflows.
• Strong technical communication and documentation skills.

Responsibilities

• Evaluate generative and agentic AI tools and advise engineering leadership on practical approaches.
• Design, deploy, and maintain internally hosted AI services and supporting infrastructure.
• Prototype AI-assisted workflows across software development, testing, documentation, and analysis.
• Integrate AI tools with engineering systems such as Jira, Confluence, Jenkins, and code repositories.

Job description

Carnegie Mellon University’s National Robotics Engineering Center (NREC) seeks a Senior Applied AI Infrastructure Engineer to lead evaluation, deployment, and integration of secure generative AI tools and self-hosted AI services across engineering workflows. You will enable AI-assisted workflows, ensure infrastructure reliability, and support multi-GPU model serving.

You will collaborate with leadership and multidisciplinary teams to ship practical AI infrastructure that powers robotics