AI Orchestration Engineer - Computer Science

Role Overview

This position sits at the point where advanced artificial intelligence meets the operational backbone of a university. Reporting to the IT Manager, you will design and deploy AI systems that streamline administrative and academic workflows, reduce manual effort, and enable secure, context-aware decision-making across the institution. Day to day, you will work directly with faculty and administrators to identify high-friction processes, then build and maintain the pipelines, models, and guardrails that turn those workflows into reliable automated solutions.

Key Responsibilities

• Design and build orchestration pipelines that connect multiple AI models, internal APIs, and university data sources, using Model Context Protocol (MCP) standards to keep AI agent interactions consistent across the entire technology ecosystem.
• Develop multimodal AI pipelines that handle text, images, audio, and documents, supporting a wide range of academic and administrative use cases.
• Create and maintain a knowledge graph layer integrated with retrieval-augmented generation (RAG) pipelines, improving the AI's ability to reason over interconnected institutional data.
• Deploy and manage on-premise or edge inference solutions for workflows that require sensitive data to stay within university infrastructure.
• Implement AI safety, security, and guardrail frameworks, including output validation and protections against prompt-injection attacks, to ensure responses meet institutional and ethical standards.
• Architect multi-agent systems where specialised AI agents handle distinct tasks and collaborate to solve complex problems.
• Develop advanced prompt strategies and chain-of-thought routines that enable reliable reasoning and accurate task execution by AI agents.
• Collaborate with university staff and faculty to analyse manual, repetitive workflows, then design automation solutions and integrate them into daily operations.

Requirements & Qualifications

• Bachelor's degree (or higher) in Computer Science, Artificial Intelligence, Data Engineering, or a closely related field.
• At least 3 years of practical experience in AI/ML engineering, data engineering, or software development with a strong focus on AI.
• Proven track record of building and deploying LLM-powered applications in real production environments, not just proof-of-concept projects.
• A strong portfolio or GitHub repository that demonstrates hands-on work with RAG pipelines, agentic systems, or complex AI orchestration workflows.
• Hands-on experience working within agile or scrum teams, with a clear understanding of sprint planning, standups, and iterative delivery.
• Familiarity with MCP, multimodal processing, knowledge graphs, and edge/on-premise inference is strongly preferred, though a demonstrated ability to learn quickly and apply these technologies will be considered.

What We Offer / Why Join

You will have the rare opportunity to shape how a university adopts AI at an institutional level, working on systems that directly improve the daily experience of staff, faculty, and students. The role offers exposure to a broad range of AI disciplines, from orchestration and multimodal pipelines to safety and multi-agent design, all within a collaborative higher-education environment. If you value meaningful impact, intellectual variety, and the chance to set technical standards that others will follow, this position provides a compelling platform to grow.