Our client is seeking a creative and analytical AI Prompt Engineer to join their IT team. The AI Prompt Engineer will be responsible for designing, optimizing, and maintaining the prompts and interactions that power the university's large language model (LLM) applications across service desk automation, administrative assistance, and other AI driven services. This role is critical in ensuring AI systems communicate effectively, produce accurate outputs, and deliver consistent user experiences across all university stakeholders. The position requires expertise in natural language processing, LLM capabilities, domain ­specific knowledge, and the ability to translate business requirements into effective AI interactions. 24/7 business continuity: This role requires occasional availability outside of traditional working hours to address urgent issues related to AI system outputs and interactions. Responsibilities include responding to critical prompt failures, addressing unexpected AI behavior, and implementing emergency prompt adjustments when systems produce incorrect or inappropriate responses. The ideal candidate must be able to quickly assess problematic AI outputs, design prompt fixes, and implement solutions to maintain service quality and organizational trust in AI systems, regardless of when issues arise. Other duties as required: This role requires flexibility in performing duties outside of the primary responsibilities to support the evolving AI ecosystem. The ideal candidate must be adaptable and willing to take on additional tasks or projects as required, such as contributing to AI training materials, participating in user feedback sessions, collaborating on new use case development, or assisting with prompt security assessments. The ability to pivot quickly to address emerging needs and contribute to the broader AI implementation strategy is essential for success in this dynamic environment. Hybrid work schedule: This role is hybrid and in the office a minimum of three days a week to facilitate collaboration with both technical teams and business stakeholders. In­ office presence enables direct observation of how users interact with AI systems, participation in cross functional workshops to refine prompts, and real-time collaboration with domain experts and subject matter specialists. This direct interaction is invaluable for understanding the nuanced language needs of different university departments and creating effective prompt strategies that align with their specific contexts. Minimum Qualifications LLM Expertise: Deep understanding of large language model capabilities, limitations, and optimal interaction patterns, with demonstrated experience designing effective prompts for enterprise applications. Prompt Engineering Skills: Advanced proficiency in crafting, testing, and refining prompts that produce consistent, accurate, and appropriate AI outputs across diverse use cases and user types. Natural Language Processing: Strong understanding of NLP concepts and techniques, including context management, semantic analysis, entity recognition, and conversational design. Systematic Testing: Experience designing comprehensive test cases and evaluation frameworks to assess prompt effectiveness, identify edge cases, and ensure consistent AI system performance. Domain Adaptation: Ability to adapt general AI models to specific domains through effective prompt strategies, content framing, and domain­ specific terminology integration. Technical Understanding: Sufficient technical knowledge to collaborate effectively with AI engineers and operations specialists on prompt implementation, optimization, and troubleshooting. Analytical Skills: Strong analytical capabilities to evaluate prompt performance data, identify patterns in AI responses, and implement data­ driven improvements to interaction designs. Creative Problem ­Solving: Creativity in developing novel prompt approaches to overcome model limitations and address complex use cases not easily handled by standard methods. Content Design: Experience creating clear, structured content frameworks that guide AI systems to produce well ­organized, user-friendly outputs in appropriate formats. User Experience Focus: Strong user centric mindset with the ability to design AI interactions that feel natural, helpful, and aligned with user expectations and needs. Documentation Skills: Excellence in documenting prompt strategies, design patterns, and guidelines to ensure consistency across the organization and enable knowledge transfer. Cross functional Collaboration: Demonstrated ability to work effectively with diverse stakeholders, including technical teams, subject matter experts, and end-users to gather requirements and refine AI interactions. Communication Skills: Excellent verbal and written communication skills to explain prompt engineering concepts to non­technical audiences and translate business requirements into technical specifications. Ethical Awareness: Understanding of ethical considerations in AI interactions, including bias mitigation, fairness, transparency, and appropriate handling of sensitive topics. Quality Assurance: Meticulous attention to detail and commitment to quality in ensuring AI outputs meet organizational standards and user expectations. Bachelor's degree in Linguistics, Computational Linguistics, Computer Science, or related field; advanced degree preferred. Minimum of 2 years of experience working with large language models and prompt engineering, with demonstrated success in enterprise applications. Experience in higher education or similar complex organizational environments preferred. Key Responsibilities & Accountabilities 1. Prompt Design and Optimization Design, develop, and continuously optimize prompts for university AI systems across service desk automation, administrative assistance, and other applications. Create prompt strategies that produce accurate, consistent, and contextually appropriate AI outputs aligned with business requirements and user expectations. 35% of time 1. Testing and Quality Assurance Develop and implement comprehensive testing frameworks to evaluate prompt effectiveness across diverse scenarios and edge cases. Systematically test AI responses to ensure accuracy, safety, appropriateness, and alignment with university policies and standards. Identify and address prompt vulnerabilities and failure modes. 25% of time 1. Domain­ Specific Adaptation Collaborate with subject matter experts to adapt AI interactions for specific university domains, including IT support, administrative processes, and academic contexts. Incorporate domain­specific terminology, workflows, and knowledge into prompt designs to enhance relevance and effectiveness. 20% 1. Performance Analysis and Improvement Analyze AI system outputs and user feedback to identify patterns, issues, and opportunities for improvement. Implement data­ driven refinements to prompts based on performance metrics and real-world usage patterns. Monitor for and address model drift or inconsistencies in AI responses over time. 10% 1. Documentation and Knowledge Sharing Create and maintain comprehensive documentation of prompt design patterns, strategies, and best practices. Develop prompt libraries, templates, and guidelines to ensure consistency across the organization. Share knowledge and insights with technical teams and stakeholders to advance organizational prompt engineering capabilities. 10% Hybrid 3 days onsite