Senior Applied AI Infrastructure Engineer - NREC

At the National Robotics Engineering Center (NREC), it is our engineers and technicians who drive the breakthroughs that define our success. The members of our technical staff collaborate closely with leadership and multidisciplinary teams to design, build, and deploy sophisticated robotic solutions that address complex challenges in industrial, commercial and government sectors. Each project benefits from their expertise, creativity, and hands-on problem-solving, fueling progress and innovation across the organization.

As part of our dedicated team, you will work alongside world-class robotics professionals committed to pushing the boundaries of technology and redefining ideas into solutions for real-world applications. We foster a culture of professionalism, respect, and collaboration, offering a flexible and encouraging environment where you can sharpen your skills, lead impactful projects, and take control of your career development.

We are seeking a dynamic Senior Applied AI Infrastructure Engineer to lead and contribute to the evaluation, deployment, and integration of secure generative and agentic AI tools, LLMs, and support of self-hosted infrastructure across engineering workflows. This is an exciting opportunity for someone who thrives in a fast-paced and innovative setting. In this role, you will be instrumental in advancing internal AI-assisted workflows, infrastructure reliability, and secure multi-GPU model serving, ensuring our team delivers exceptional and groundbreaking results.

Your primary responsibilities include: 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.

Measuring 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.

Required Qualifications: 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. 3 or more of the following: Experience deploying and maintaining software services. Experience with containers and reproducible deployment tools such as Docker.