Teaching Faculty in Applied Artificial Intelligence

About this position

Teaching Faculty in Applied Artificial Intelligence Location: UTK Knoxville, Tennessee Open Date: Sep 7, 2026 Description: The College of Emerging and Collaborative Studies (CECS) at the University of Tennessee, Knoxville's (UTK) seeks a dynamic, collaborative, and innovative faculty member to contribute to its existing and future programs in Artificial Intelligence. CECS has one position open in Artificial Intelligence for a non-tenure track, nine-month, full-time appointment, on campus, beginning January 1, 2027. This is an open-rank search; appointment at the Teaching Assistant Professor, Teaching Associate Professor, or Teaching Professor level will be commensurate with qualifications and experience. The selected candidate will be responsible for teaching and service, with assignments made by the dean according to enrollment demands and scheduling. Primary teaching responsibilities will include courses in Artificial Intelligence spanning introductory through graduate-level offerings, as well as other new courses launched by the College. We are seeking a colleague who brings deep applied expertise in one or more artificial intelligence domains and who shares our commitment to education that is hands-on, intercollegiate, and workforce-relevant. Candidates are expected to maintain a scholarship focused on practice and impact; traditional academic research is welcome but not required. Expertise in the following teaching areas is expected: Artificial Intelligence: The College is especially interested in candidates whose primary strength lies in generative AI and large language model engineering, agentic AI and multi-agent systems, AI systems evaluation and safety, or AI forward deployed engineering — including retrieval-augmented generation, LLM fine-tuning, agent orchestration and tool use, prompt engineering, monitoring and evaluation of production AI systems (robustness testing, red-teaming, and hallucination detection), and the applied, client-facing deployment of AI solutions in real-world settings — as these are current strategic priorities for the program. Beyond that focus, expertise is also expected in: introductory artificial intelligence concepts and applications; natural language processing and conversational AI; human-AI interaction and user experience design; AI-based data handling, preprocessing, and visualization; and programming in Python using frameworks such as Scikit-learn, TensorFlow, and PyTorch. The ideal candidate will bring the knowledge and skills to teach courses such as Agentic AI and Multi-Agent Systems, Generative AI and Large Language Model Engineering, AI Systems Evaluation and Safety, and AI Forward Deployed Engineering, should the program choose to offer them in the future. Key Responsibilities • Teach courses spanning introductory through graduate level in Artificial Intelligence, including lab-intensive and applied learning components • Develop and regularly update course materials to reflect current tools, frameworks, and industry practice • Collaborate with intercollegiate program faculty to design integrative learning experiences that connect technical skills with ethical, policy, and real-world application contexts • Advise and mentor students, including supervision of capstone projects and applied research • Maintain an active applied scholarly or professional practice profile relevant to your specialization; traditional academic research is welcome but not required • Contribute to program assessment, continuous improvement, and accreditation processes • Participate in college governance, committees, and professional community engagement Qualifications: • Ph.D. in Artificial Intelligence, Computer Science, Data Science, or a closely related field • Demonstrated expertise in one or more of the artificial intelligence teaching areas listed above • Evidence of effective teaching at the university level, or substantial practitioner experience with instructional or mentoring responsibilities • Experience or demonstrated ability in the design and delivery of courses in multiple formats, including face-to-face, synchronous online, asynchronous online, and hybrid modalities • Experience with technology-enhanced teaching and team-based instructional practices • Commitment to applied, hands-on, and workforce-oriented undergraduate education Preferred Qualifications • Significant professional experience in a relevant industry or applied context (highly desirable) • Relevant industry certifications, where applicable • Record of applied scholarship: professional publications, conference presentations, tool or open-source software development, or practice-based projects • Experience developing or delivering simulation-based, lab-intensive, or capstone learning experiences • Demonstrated ability or interest in teaching across disciplinary boundaries (e.g., applied AI and data science, or applied AI and a domain application such as cybersecurity or health data) • Familiarity with curriculum development, program assessment, or accreditation processes (e.g., ABET, SACSCOC) • Experience mentoring students from diverse backgrounds in technical fields Faculty Rank Criteria Appointment rank will be determined based on the following criteria: • Teaching Assistant Professor – Holds a Ph.D. (or will hold at the time of appointment) in a related field with promise for excellence in teaching and related responsibilities, evidenced by early effectiveness and contributions. • Teaching Associate Professor – Terminal degree with a demonstrated record of excellence in teaching and related responsibilities, with a minimum of three years of full-time teaching experience in a related field. • Teaching Professor (Full) – Terminal degree with a sustained, consistent record of excellence and evidence of instructional leadership (e.g., curriculum development, mentoring, pedagogical innovation) commensurate with senior rank. Note: Applicants must be authorized to work in th

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