AI Product Engineer (KTP Associate)

The Odyssey Trust Company in partnership with Ulster University, has established a 2-year Knowledge Transfer Partnership (KTP) Project and is offering an exciting opportunity for an ambitious and highly motivated Computer Science/Software Engineering graduate to work as a KTP Associate to oversee the development of an AI-powered decision support platform to drive proactive decision-making, ultimately leading to improved customer experience, reduced operational costs, and increased revenue.

Job Purpose

The KTP Associate will undertake the complex task of developing the Agentic AI-powered Situation Awareness Platform (AI-SAP) to be an intelligent and integrated decision-support platform that can handle large and complex datasets from diverse sources, provide actionable, real-time insights and predictive capabilities, and allow users to interactively query complex data and make more effective business decisions. The role involves utilising skills in Agentic AI, machine learning, and data engineering, It also involves working with stakeholders (academics and industry) and managing the project to create a robust system to support the company’s strategic and operational goals. The Associate will need to advocate for the project’s importance and convince stakeholders of its value, thus highlighting the potential benefits it brings to the business, targeted development partners and clients.

Main Duties

• Develop a detailed understanding of the OTC’s business operations, diverse data sources, and stakeholder needs through comprehensive interviews and system mapping, to define project scope, prioritize use cases, and design the solution architecture.
• Design, build, and deploy a secure and scalable cloud-based data infrastructure (e.g., on Microsoft Azure) and develop robust ETL/ELT pipelines to integrate, cleanse, and unify priority data from disparate company systems.
• Develop and deploy initial real-time, interactive dashboards to provide immediate insights from the integrated data, thereby demonstrating early project value and gathering user feedback for iterative improvement.
• Apply advanced data science techniques to develop, train, and validate predictive models for key business challenges like attendance forecasting and customer segmentation. Furthermore, design and prototype novel agentic AI systems to provide automated analysis and decision support.
• Lead the embedding of the new technologies by designing and delivering training workshops, creating user documentation, and championing a data-driven culture across OTC to upskill staff and ensure long-term adoption and sustainability.
• Manage the day-to-day progress of the KTP project, ensuring milestones are met, and effectively communicate progress and findings to all stakeholders through Local Management Committee (LMC) meetings, reports, and presentations.
• Continuously refine and expand the Agentic AI-powered Situation Awareness Platform (AI-SAP) based on user feedback and performance data, culminating in the delivery of a fully operational platform and a strategic roadmap for future AI development at OTC.

Skills and Experience

Applicants should hold an honours degree in a Computing based subject including Computer Science, Software Engineering, Data Science, Artificial Intelligence, or a related numerate discipline with significant programming/computing components.

The Associate should have good skills and experience in the following areas:

• Knowledge and experience of using research methods (and understanding of qualitative and quantitative research) and/or being involved in research
• Proven experience in ML/data analytics and AI projects
• Proficient in programming languages such as Python
• Experience contributing to progress within software development and/or research projects
• Knowledge of advanced computing topics, e.g., AI, ML, and LLMs, etc

The selection process will also require candidates to complete an unseen technical task on the day of the interview.
Further information can be found in the Candidate Briefing Document.

The appointment will be for a period of two years and although the post is administratively linked to the University, the Associate will be based at the Company’s premises at 2 Queens Quay, Belfast BT3 9QQ. Successful completion of the KTP may lead to a new position within the company partner at the end of the project period.

Both the Odyssey Trust Company and Ulster University are Equal Opportunities employers.

Benefits

This KTP project provides a unique opportunity to apply your knowledge and skills to a real industry challenge whilst benefitting from industrial and academic supervision and mentoring from Ulster University’s experts Dr Tazar Hussain, Professor Jun Liu, and Professor Chris Nugent who will enhance your technical expertise in Agentic AI, ML/Data analytics, Large Language Models (LLM), and their implementations. As a KTP Associate you will also acquire new skills in strategic leadership that can help to super-charge your career. Additional benefits include:

• Generous £11.8k Training, Development, equipment & Travel Budget dedicated to project delivery, which include but not limited to trainings and courses to enhance research skills as well as for critical networking with key players in the field, specific project equipment etc
• FREE formal management training and development
• FREE University postgraduate part-time study opportunities at Ulster University (where appropriate)
• 10% Associate development time built into work plan
• Opportunity to work with academics to develop scientific articles to be published in peer-reviewed, world-class academic journals
• Competitive salary
• Enhanced skills and experience that are marketable and highly transferable
• KTPs accelerate careers

This KTP will focus on developing an Agentic AI-powered Situation Awareness Platform (AI-SAP) to be an intelligent and integrated decision-support platform that can handle large and complex datasets from diverse sources, provide actionable, real-time insights and predictive capabilities, and allow users to interactively query complex data and make more effective business decisions. The project involves utilising skills in Agentic AI, LLM, machine learning, data engineering and software development.