AI Orchestration Engineer - Computer Science

Role Overview

You will join a university IT team as the technical lead for AI orchestration, bridging the gap between advanced language models and everyday institutional operations. Your work will involve building the pipelines, governance layers, and agent frameworks that let faculty and administrators safely automate complex workflows across the institution. Each day you will move between coding, system design, and conversations with non-technical staff — turning their manual frustrations into reliable, AI-assisted processes.

Key Responsibilities

• Build orchestration pipelines that route requests across multiple AI models, internal APIs, and university databases, using the Model Context Protocol (MCP) to create a consistent interface for tools and data sources.
• Develop multimodal AI pipelines that handle text, images, audio, and documents so that a single workflow can support diverse academic and administrative use cases.
• Construct and maintain a knowledge graph layer that connects siloed university data, and integrate it with retrieval-augmented generation (RAG) pipelines to improve contextual accuracy in AI answers.
• Deploy and manage on-premise or edge inference environments for sensitive datasets that are not permitted to leave institutional infrastructure.
• Implement guardrails, output validation, and prompt-injection defences so that AI responses align with institutional policy and ethical guidelines.
• Design multi-agent systems where specialised AI agents tackle distinct tasks, coordinate handoffs, and resolve conflicts in their outputs.
• Develop sophisticated prompt strategies and chain-of-thought routines that enable reliable reasoning on complex, multi-step problems.
• Collaborate with university administrators and faculty to identify high-friction manual workflows with strong automation potential, then prototype and iterate on solutions.

Requirements & Qualifications

• A minimum of a Bachelor’s degree in Computer Science, Artificial Intelligence, Data Engineering, or a related discipline.
• At least three years of hands-on experience in AI/ML engineering, data engineering, or software development with a strong AI focus.
• Demonstrable experience shipping LLM-powered applications to production, including monitoring, versioning, and performance tuning.
• A portable portfolio or GitHub repository that showcases work on RAG pipelines, agentic systems, or complex AI orchestration.
• Mandatory experience working in agile or scrum environments, with the ability to break down ambiguous problems into sprint-sized deliverables.
• Familiarity with knowledge graphs, vector databases, and modern orchestration tooling is strongly preferred.
• Comfortable speaking with non-technical stakeholders to translate operational pain points into concrete AI requirements.