School of Nursing Full Stack AI Engineer

The School of Nursing at The University of Texas at Austin seeks a Full Stack AI Engineer to design, deploy, and maintain dependable AI applications that support nursing education and faculty research. The role involves turning teaching objectives and research protocols into secure, accessible software and continuing evaluation and maintenance beyond an initial prototype.

Responsibilities

• Build and maintain applications using Python, TypeScript, and React or Next.js, including backend APIs, databases, authentication, role-based access, and deployment in university-approved cloud environments.
• Implement production large language model (LLM) applications using retrieval-augmented generation (RAG), including source citations, tool calling, and guardrails.
• Evaluate retrieval quality, factual grounding, citation accuracy, unsafe outputs, latency, and cost both before and after release.
• Ensure factual answers are traceable to approved sources and source versions.
• Provide clear handling for insufficient or conflicting evidence, including declining unsupported answers and directing questions to human review.
• Develop faculty authoring, content approval, and review tools.
• Translate educator-authored rubrics into measurable criteria, assessment logic, and evaluation cases, including faculty validation and review of AI-generated feedback.
• Translate investigator-approved protocols into application features, including study-specific workflows, approved intervention content, participant interactions, and reproducible data collection and exports.
• Implement protocol amendments in coordination with the research team.
• Build learning assessments, learner progress tracking, and dashboards for educational outcomes, engagement, intervention delivery, and study measures.
• Separate educational records from research datasets and permissions as required by each project.
• Implement automated tests, continuous integration and deployment (CI/CD), monitoring, backups, and recovery procedures.
• Maintain version control for code, prompts, source materials, rubrics, assessments, and AI model configurations.
• Apply access controls, secure secret management, data minimization, and safe logging.
• Protect student records, research data, and any inadvertently entered patient information through university-approved procedures and services.
• Scope projects with faculty, document architecture and operating procedures, train users, resolve technical issues, and coordinate with university IT, security, accessibility, and research support teams.

Requirements

• Bachelor’s degree in computer science, software engineering, information systems, or a related field, or an equivalent combination of relevant education and experience, subject to the approved university job classification.
• Demonstrated proficiency in Python and TypeScript, and experience developing applications with React or Next.js, backend APIs, relational or other appropriate databases, authentication, Git, and cloud deployment.
• Demonstrated experience deploying and maintaining at least one LLM-enabled application used by people beyond its development team, with responsibility for reliability and iteration.
• Practical experience with RAG, citations, tool calling, guardrails, and repeatable LLM evaluations, including recognition of hallucinations, prompt injection, and limitations of AI-generated assessments.
• Experience with automated testing, monitoring, and CI/CD, and knowledge of application security, privacy, and accessibility fundamentals.
• Ability to translate faculty-defined learning objectives, rubrics, and research requirements into testable software behavior and to communicate technical choices clearly to nontechnical collaborators.
• Ability to manage competing priorities, estimate effort, document decisions, and maintain software through deployment and ongoing support.

Preferred Qualifications

• Current enrollment in, or completion of, a master’s program in artificial intelligence, computer science, data science, or a related field.
• Experience with nursing or health professions education, virtual patient simulation, digital health interventions, or human participant research.
• Experience building content-management, educator-review, content-approval, learning assessment, and analytics workflows.
• Experience with Canvas or Moodle integration, LTI, xAPI, or SCORM, and with university identity and access systems.
• Experience implementing accessible interfaces, including keyboard and screen-reader access, and age-appropriate, user-centered user experiences.

Technologies

• Python
• TypeScript
• React
• Next.js
• Large language model
• Retrieval-augmented generation (RAG)
• Tool calling
• Git
• CI/CD
• Cloud deployment
• Authentication
• Relational databases

Working Expectations and Oversight

• Faculty retain responsibility for educational content, clinical accuracy, rubrics, assessment interpretation, and approval of source materials.
• Investigators retain responsibility for research protocols, consent language, and required research approvals.
• The engineer implements approved requirements and flags technical risks, gaps, and limitations.
• Applications must clearly distinguish educational simulation and research activities from real-patient diagnosis or treatment.
• Any research intervention that includes health guidance must remain within its approved protocol, with defined human oversight and escalation pathways.
• The supervisor and faculty partners will prioritize a manageable project portfolio within the 30-hour workweek, including maintenance and support.
• Work schedule, reporting relationship, formal job classification, benefits, and exemption status will be finalized through university HR.

Salary

$70,000 annual for a 30 hour assignment.

Required Application Materials

• Submit a resume, a brief statement describing relevant experience, and examples of deployed applications or a portfolio.
• Describe your individual contribution, deployment approach, evaluation methods, and maintenance responsibilities.
• Confidential or proprietary materials are not required.

Application Instructions

Applicants who are not current university employees or contingent workers: You will be prompted to submit your resume the first time you apply. For subsequent applications, you will be provided an option to upload a new Resume. Any additional Required Materials (letter of interest, references, etc.) will be uploaded in the Application Questions section using file multi-select. Confirm all Required Materials are uploaded before submitting. After submission, you cannot make changes.

Current university employees and contingent workers: You MUST apply within Workday by searching for Find UT Jobs. If you are a current University employee, log in to Workday and update your Professional Profile sections before applying. The application is one page and prompts you to upload your resume. You must also respond to application questions to upload additional required materials identified above.

Employment Eligibility

• Regular staff employed in their current position for the last six continuous months are eligible for openings recruited through University-Wide or Open Recruiting, including promotional opportunities and lateral transfers.
• Promotion or transfer eligible staff may apply for positions without supervisor approval.

Retirement Plan Eligibility

The retirement plan for this position is Teacher Retirement System of Texas (TRS), subject to the position being at least 20 hours per week and at least 135 days in length.

Background Checks

A criminal history background check will be required for finalist(s) under consideration for this position.

Employment Eligibility Verification (I-9) and E-Verify

• If hired, you must complete the federal Employment Eligibility Verification I-9 form and present acceptable, original documents proving identity and authorization to work in the United States no later than the third day of employment.
• Failure to do so results in loss of employment.
• The University of Texas at Austin uses E-Verify for all new hires effective May 2015. Company ID number: 854197. For more information, see the E-Verify Poster and Right to Work Poster materials referenced in the posting.

Compliance

• Employees may be required to report violations of law under Title IX and the Jeanne Clery Disclosure of Campus Security Policy and Crime Statistics Act (Clery Act).
• If the position is identified as a Campus Security Authority (Clery Act), you will be notified and provided resources for reporting.
• The Clery Act requires prospective employees be notified of the availability of the Annual Security and Fire Safety report. The most recent report can be accessed online, or a copy can be obtained from University Compliance Services, 1616 Guadalupe Street, UTA 2.206, Austin, Texas 78701.