Edtech.com's Summary

University of Texas at Austin is hiring a School of Nursing Full Stack AI Engineer. The role builds and maintains AI applications for nursing education and faculty research, such as virtual patient simulations, educational assistants, and research chatbots, working with educators and investigators to turn their teaching goals and research protocols into secure, accessible software. It is a 30-hour-per-week staff position expected to continue until August 31, 2028.

Highlights

Build and maintain applications in 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 applications using retrieval-augmented generation, source citations, tool calling, and guardrails, and evaluate retrieval quality, factual grounding, unsafe outputs, latency, and cost before and after release.
Make factual answers traceable to approved sources and decline unsupported answers by routing them to human review when evidence is insufficient or conflicting.
Develop faculty authoring, content approval, and review tools, and turn educator-written rubrics into measurable criteria and assessment logic with faculty validation.
Translate investigator-approved research protocols into application features, including study workflows, participant interactions, and reproducible data collection and exports.
Build learning assessments, progress tracking, and dashboards, keeping educational records separate from research datasets.
Implement automated tests, CI/CD, monitoring, backups, and recovery procedures, and apply access controls and safe logging to protect student records and research data.
Requires a bachelor’s degree in computer science or a related field (or equivalent), proficiency in Python and TypeScript, and experience deploying at least one LLM-enabled application used beyond its development team.
Preferred qualifications include master’s-level study in AI or computer science, nursing or health professions education experience, Canvas or Moodle integration, and accessible interface design.
Part-time at 30 hours a week with a $70,000 annual salary for the 30-hour assignment; applicants submit a resume, a brief experience statement, and examples of deployed applications.

School of Nursing Full Stack AI Engineer Full Description

School of Nursing Full Stack AI Engineer

The University of Texas at Austin, School of Nursing. UT Main Campus. Job requisition ID R_00049526. Part time, 30 weekly scheduled hours. Non-exempt from FLSA. Earliest start date: immediately. Position duration: expected to continue until Aug 31, 2028. Open to all applicants. Salary: $70,000 annual for 30 hour assignment.

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.

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.

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

Let me find your next job.

Thanks - you're signed up.

School of Nursing Full Stack AI Engineer

Edtech.com's Summary

School of Nursing Full Stack AI Engineer Full Description

Network Infrastructure Engineer

Application Integration Developer - Library IT

Senior Software Engineer

Cloud Developer

Machine Learning Engineer