Develop technical source material for multiple sequential learning modules including code examples, system architecture references, case studies, assessment briefs, and worked solutions
Collaborate with learning designers to translate curriculum frameworks into technically accurate and production-relevant learning content
Participate in collaborative curriculum design sessions with academic contributors to refine programme scope, sequencing, and exit standards
Review learning materials throughout the development process to ensure technical accuracy, credibility, and alignment with current industry practices
Design summative assessments that reflect real-world AI engineering hiring and evaluation formats
Provide practitioner insight into the design of production AI application architectures and engineering workflows
Contribute expert insights to live masterclass or knowledge-sharing sessions following programme launch
Advise on the practical realities of building and deploying production AI applications, including common failure modes and operational challenges
Support the development of content covering evaluation methodologies, reliability patterns, governance considerations, and architectural decision-making for AI applications

Demonstrated experience building and deploying production AI systems powered by large language models in recent industry environments
Strong software engineering foundations including testing practices, CI/CD pipelines, observability, and reliability engineering approaches
Practical experience implementing LLM-based application architectures including API integrations, prompt strategies, and structured outputs
Hands-on experience with retrieval-augmented generation pipelines including embedding models, vector databases, hybrid search approaches, and retrieval evaluation
Familiarity with agent-based architectures including planning loops, memory strategies, tool integrations, and cost management patterns
Experience designing evaluation frameworks for AI applications including automated evaluation methods, regression testing approaches, and performance monitoring
Understanding of responsible AI practices including bias considerations, governance documentation, and auditability
Experience mentoring engineers, hiring technical talent, or assessing engineering capability in AI-related roles
Ability to collaborate effectively with technical and academic contributors on curriculum and learning design
Professional experience demonstrating subject expertise equivalent to postgraduate level knowledge in AI engineering or related fields

Collaborative, people-centred performance culture
Opportunities to grow in a fast-paced environment
Flexible remote working arrangements
Opportunity to contribute expertise to the development of high-impact professional learning experiences

AI Engineering Subject Matter Expert – Contract

Key skills