I. About the UP-AI Centre:

The UP-AI Centre is the University of Puthisastra’s institutional centre for artificial intelligence, digital innovation and responsible AI-enabled transformation. It is being developed as a university-wide platform connecting AI, education, health, research, institutional data, digital transformation and public good. Its working framework, AI-HELP: AI for Health, Education, Learning & Public Good, reflects UP’s intention to use AI not simply as a technology, but as a means of strengthening learning, research, institutional decision-making, operational effectiveness and societal impact.

The Centre has three overarching knowledge functions:

• Accumulate Knowledge: Strengthen how UP gathers, organises and uses institutional data, evidence and knowledge.
• Share Knowledge: Build AI capability among staff and students and translate technical developments into practical, responsible and accessible applications.
• Create Knowledge: Generate research, innovation, pilots, publications, partnerships and new intellectual outputs through applied AI.

II. Purpose of the Role

The AI Implementation and Innovation Fellow will translate the UP-AI Centre’s approved priorities into practical, sustainable and measurable applications. The role will identify university processes where AI, automation and digital tools can improve effectiveness, reduce repetitive work, strengthen teaching and learning, or create new opportunities for students and staff.

The Fellow will work across academic and professional service functions to understand and map existing workflows, prioritise appropriate use cases, lead or support the design and configuration of potential solutions, test these in controlled environments, and support their responsible adoption. Work will begin with practical, lower-risk and achievable applications that can demonstrate value and build institutional confidence before progressing to more complex integrations.

The Fellow will apply a design-document-teach-transfer approach: identifying suitable opportunities; developing and testing solutions; documenting their operation, governance and maintenance; training staff and students; and transferring ongoing ownership to an appropriate UP unit. The objective is to build institutional capability rather than dependence on an individual specialist.

This is an implementation-focused position and does not carry responsibility for the strategic leadership or management of the UP-AI Centre. The Fellow will work under the direction of the Manager, UP-AI Centre, within priorities approved through UP’s AI governance structures.

III. Duties and Responsibilities:

The overall job description below outlines the main areas of responsibilities of the Employee: The precise work program will be jointly developed with the successful candidate during the first weeks of appointment. However, the role is expected to contribute to the following core outputs.

Output 1. University Workflow and AI Opportunity Mapping

Undertake structured engagement with academic and professional services units to understand and document selected end-to-end workflows. Identify repetitive, stable or inefficient processes and prioritise opportunities for responsible AI-assisted improvement according to value, simplicity, risk, sustainability and institutional readiness.

Output 2. Applied AI and Automation Implementation

Lead or support the design, configuration, testing and implementation of practical AI-enabled workflows, agents, automation and related digital applications aligned with approved UP-AI priorities. Work with technical specialists where required and emphasise useful, maintainable deployment rather than experimentation for its own sake.

Output 3. AI-Enabled Learning and Teaching

Work with Digital Learning and academic faculties to identify, pilot and evaluate responsible applications of AI in curriculum development, learning design, assessment support, Moodle, staff capability development and student learning.

Output 4. Documentation and Sustainability

For each approved implementation, prepare appropriate process schematics, technical and user documentation, operating instructions, maintenance guidance, training resources, system ownership arrangements and change records. Store documentation in an approved UP repository so that successful applications can be maintained and developed by UP.

Output 5. AI Training and Capability Development

Contribute to UP’s AI Fluency and capability-building program. Subject to academic approval and workload allocation, contribute to or deliver an applied AI course or equivalent structured learning program each semester. Develop short practical workshops based on successful UP use cases and support staff and students to replicate selected functions in safe test environments.

Output 6. Student Engagement and Applied Innovation

Work with relevant faculties to develop structured opportunities for students to participate in applied AI projects, testing and innovation while maintaining appropriate supervision, data protection and academic quality.

Output 7. Evaluation, Research and Knowledge Creation

Evaluate major implementation activities and, where suitable, translate them into research, publications, case studies or other knowledge outputs. Where mutually beneficial and formally approved, activities may inform the Fellow’s higher-degree research, subject to separate supervision, ethics, data-access,authorship and intellectual-property approvals.

Output 8. UP-AI Reporting

Maintain clear and timely reporting of activities, risks, milestones and deliverables through the UP-AI output tracker and other agreed reporting mechanisms.

Indicative First-Year Delivery Framework

During the first four to six weeks, the successful candidate will work with the Manager, UP-AI Centre, and relevant stakeholders to complete an initial assessment. Within six weeks, the Fellow will submit a Year 1 workplan containing agreed priorities, deliverables, milestones, measures of benefit, implementation risks, responsible system owners and reporting dates for approval by the Manager, UPAI Centre.

Specific numerical targets will be finalised following the initial mapping exercise. Evaluation measures should include, where relevant, time saved, reduction in repetitive work, reliability, user adoption, cost, privacy and security risk, maintainability, training participation and successful transfer to a designated UP owner.

Governance and Responsible AI

All activity must operate within University governance and the UP AI policy framework. The Fellow will work closely with relevant institutional functions where projects involve student or staff data, confidential institutional information, learning-management systems, financial systems, authentication, research information, external platforms and APIs, cybersecurity, procurement or intellectual property.

Solutions involving institutional systems, authentication, financial information, Moodle, staff or student data, or external APIs must undergo appropriate technical, privacy, cybersecurity, procurement and operational review before connection to live systems. No pilot may enter operational use without an identified UP system owner, approved access controls, maintenance arrangements, documented fallback procedures and required institutional approval.

The Fellow will develop safe student-test or sandbox environments where staff and students can replicate selected AI-assisted functions without accessing live institutional systems or confidential data. Where appropriate and approved, the Fellow may also develop video demonstrations and reusable learning resources, subject to UP requirements concerning consent, confidentiality, cybersecurity and intellectual property.

The role may contribute technical and implementation expertise to the UP-AI Technical Working Group when requested but will not replace the Centre’s existing strategic, governance or management leadership.
Person Specification
Essential

• Relevant postgraduate qualification or equivalent professional experience in digital transformation, artificial intelligence, information technology, education, business or a related field.
• Demonstrable practical experience using contemporary AI technologies.
• Experience implementing or prototyping AI-assisted workflows, automation or related digital applications, independently or in collaboration with technical specialists.
• Ability to analyse and map existing processes and translate them into practical technology solutions.
• Experience delivering training and explaining complex technologies to non-specialist users.
• Strong documentation, communication, stakeholder-engagement and change-management skills.
• Ability to work collaboratively across professional and academic teams.
• Demonstrated capacity to take projects from concept through controlled implementation, evaluation and capability transfer.
• Ability to work within institutional governance, data-protection, cybersecurity and quality-assurance requirements.
• Strong commitment to responsible and ethical use of AI.

Desirable

• Higher education teaching, learning-design or professional-development experience.
• Experience with Moodle or another learning-management system.
• Experience with low-code or no-code AI development environments, workflow automation platforms, webhooks, APIs, authentication services, AI agents or systems integration.
• Experience working across different cultural or international contexts.
• Experience working with students on applied technology projects.
• Knowledge of digital transformation in universities.
• Experience evaluating or researching technology implementation.
• Experience producing video demonstrations or reusable digital training resources.
• Relevant industry, university or international networks in AI and digital innovation.

Appointment Terms and Performance Review

• This position will initially be offered as a 24-month fixed-term contract. Performance and deliverables will be reviewed at six months and formally at 12 and 24 months against the approved workplan, UP-AI priorities and agreed measures of benefit. Any continuation beyond the initial term will be subject to satisfactory performance, institutional need, funding and University approval.
• The Fellow may be required to undertake other related duties reasonably assigned by the Manager, UP-AI Centre, consistent with the purpose and level of the appointment.

IV. Minimum Qualifications and Requirements:
1. Experience:

At least five years of relevant professional experience in digital transformation, applied artificial intelligence, information technology or education, including hands-on use of contemporary AI tools. Demonstrated experience implementing or prototyping AI-assisted workflows, automation or digital applications, either independently or with technical specialists. Proven ability to analyse and map existing processes and translate them into practical, maintainable solutions. Experience delivering training and explaining complex technologies to non-specialist staff and students. A track record of taking projects from concept through controlled implementation, documentation, evaluation and handover to an operational owner, while working within institutional governance, data protection and quality assurance requirements

2. Education:

A relevant postgraduate qualification in digital transformation, artificial intelligence, information technology, education, business or a related field, or equivalent professional experience. A recognised teaching, training or learning design qualification is desirable, as are certifications in AI development environments, workflow automation platforms or learning management systems such as Moodle