Job Objective

Operating at the intersection of AI innovation and institutional operations, and reporting to the IT Manager, the role will include, but is not limited to, the following responsibilities:

Duties and responsibilities

• Architect and implement orchestration pipelines that connect multiple AI models, internal APIs, and University databases, while integrating Model Context Protocol (MCP) standards to standardise how AI agents interact with tools, APIs, and data sources across the University’s technology ecosystem. 
• Design and implement multimodal AI pipelines capable of processing text, images, audio, and documents to support diverse University use cases. 
• Develop and maintain a knowledge graph layer integrated with RAG pipelines to enhance contextual reasoning over interconnected University data. 
• Deploy and manage on-premise or edge inference solutions for data-sensitive workflows where University data must not leave institutional infrastructure. 
• Implement AI safety, security, and guardrail frameworks to enforce output validation, prevent prompt-injection attacks, and ensure AI responses meet institutional and ethical standards. 
• Design multi-agent systems where different AI agents handle specialised tasks. 
• Develop sophisticated prompt strategies and \"chain of thought\" routines to ensure AI agents perform complex reasoning tasks reliably. 
• Partner with University administrators and faculty to understand high-friction manual workflows that could be automated. 
• Any other duties that may be assigned from time to time by the supervisor or captured in your detailed job description. 

qualification and experience

• A minimum of a Bachelor's in Computer Science, Artificial Intelligence, Data Engineering, or a related field. 
• 3+ years of experience in AI/ML engineering, data engineering, or software development with a focus on AI. 
• Proven experience building and deploying LLM-powered applications in production environments. 
• A strong portfolio or GitHub repository demonstrating work with RAG pipelines, agentic systems, or complex AI orchestrations. 
• Experience working in an agile/scrum environment is mandatory.