School of Nursing Full Stack AI Engineer

The University of Texas at Austin; Utah; On-site; USD 63,000 - 77,000; Full time

Job summary

The University of Texas at Austin School of Nursing seeks a Full Stack AI Engineer to design, deploy, and maintain AI applications for nursing education and faculty research. You will build secure, scalable tools with Python, TypeScript, React/Next.js, and cloud deployment, ensuring traceability and guardrails.

Work with educators, investigators, and IT partners to translate rubrics and study protocols into production features, supporting learning analytics, content authoring, and data exports

Qualifications

• Bachelor's degree in computer science or related field or equivalent experience.
• Proficiency in Python and TypeScript with React/Next.js and cloud deployment.
• Experience deploying LLMI-based apps with reliability ownership.
• Experience with RAG, citations, tool calling, guardrails, and evaluation.
• Knowledge of CI/CD, security, privacy, and accessibility fundamentals.
• Ability to translate objectives into testable software behavior and communicate clearly.

Responsibilities

• Build and maintain applications using Python, TypeScript, React/Next.js and cloud environments.
• Implement production LLM apps with RAG, citations, tool calling, guardrails.
• Ensure source traceability and handle insufficient or conflicting evidence.
• Develop faculty authoring, content approval, and review tools.
• Translate study protocols into features, collect reproducible data, and exports.
• Create learning assessments, dashboards, and analytics while safeguarding data.
• Maintain CI/CD, tests, monitoring, backups, and version control.
• Enforce security, privacy, accessibility, and safe logging practices.
• Document architecture, train users, and coordinate with IT and research teams.

Skills

Python

TypeScript

React/Next.js

Backend APIs

Databases

Authentication

Git

Cloud deployment

RAG

CI/CD

Security & accessibility

Learning analytics

Education

Bachelor's degree in CS or related field

Tools

Canvas

Moodle

LTI/xAPI/SCORM

Job description

Job Posting Title: School of Nursing Full Stack AI Engineer ---- Hiring Department: School of Nursing ---- Position Open To: All Applicants ---- Weekly Scheduled Hours: 30 ---- FLSA Status: Non-Exempt from FLSA ---- Earliest Start Date: Immediately ---- Position Duration: Expected to Continue Until Aug 31, 2028 ---- Location: UT MAIN CAMPUS

Job Details: Purpose

The School of Nursing seeks a Full Stack AI Engineer to design, deploy, and maintain reliable AI applications supporting nursing education and faculty research. Working with educators, investigators, and university technology teams, the engineer will translate teaching goals and research protocols into secure, accessible applications that can be evaluated, maintained, and used beyond an initial prototype. Projects may include virtual patient simulations, educational assistants, research chatbots, digital self‑management interventions, and tools for content review, assessment, and analytics. This is a staff position; enrollment in a degree program is not required.

Responsibilities

• Application development: 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.
• AI engineering: Implement production large language model applications using retrieval‑augmented generation (RAG), source citations, tool calling, and guardrails. Evaluate retrieval quality, factual grounding, citation accuracy, unsafe outputs, latency, and cost before and after release.
• Source traceability: Make substantive factual answers traceable to approved sources and source versions. Build clear handling for insufficient or conflicting evidence, including declining unsupported answers and directing questions to human review.
• Education workflows: Develop faculty authoring, content approval, and review tools. Translate educator‑authored rubrics into measurable criteria, assessment logic, and evaluation cases, with faculty validation and review of AI‑generated feedback.
• Research support: 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.
• Assessment and analytics: 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.
• Reliability and maintenance: 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.
• Security and responsible use: Apply access controls, secure secret management, data minimization, and safe logging. Protect student records, research data, and inadvertently entered patient information through university‑approved procedures and services.
• Collaboration and documentation: 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.

Required qualifications

• 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. Relevant paid, research, or independently developed work may qualify.
• 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 working 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.

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

Salary $70,000 annual for 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.

Employment Eligibility

Regular staff who have been employed in their current position for the last six continuous months are eligible for openings being recruited for through University‑Wide or Open Recruiting, to include both promotional opportunities and lateral transfers. Staff who are promotion/transfer eligible 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.

Equal Opportunity Employer

The University of Texas at Austin, as an equal opportunity/affirmative action employer, complies with all applicable federal and state laws regarding nondiscrimination and affirmative action. The University is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, national origin, age, marital status, sex, sexual orientation, gender identity, gender expression, disability, religion, or veteran status in employment, educational programs and activities, and admissions.

Pay Transparency

The University of Texas at Austin will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor’s legal duty to furnish information.

Employment Eligibility Verification

If hired, you will be required to complete the federal Employment Eligibility Verification I‑9 form. You will be required to present acceptable and original documents to prove your identity and authorization to work in the United States. Documents need to be presented no later than the third day of employment. Failure to do so will result in loss of employment at the university.

E-Verify

The University of Texas at Austin use E-Verify to check the work authorization of all new hires effective May 2015. The university’s company ID number for purposes of E-Verify is 854197. For more information about E-Verify, please see the following: E-Verify Poster (English and Spanish) [PDF] Right to Work Poster (English) [PDF] Right to Work Poster (Spanish) [PDF]

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) and to support compliance. If this position is identified a Campus Security Authority (Clery Act), you will be notified and provided resources for reporting. Responsible employees under Title IX are defined and outlined in HOP-3031. The Clery Act requires all prospective employees be notified of the availability of the Annual Security and Fire Safety report. You may access the most recent report here or obtain a copy at University Compliance Services, 1616 Guadalupe Street, UTA 2.206, Austin, Texas 78701.

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