AI Engineering Subject Matter Expert (Contract) in Nottingham

At peopleworth, we support work where people and performance thrive. As part of our Employer Group, we work with a variety of forward‑thinking partners and are excited to share this opportunity that sits within our growing group.

Role Overview

We are seeking an experienced AI Engineering Subject Matter Expert to lead the technical content design of a rigorous professional learning programme focused on production AI application engineering. This role focuses on defining, developing, and validating technical learning materials that reflect how modern AI systems are designed, deployed, evaluated, and governed in real‑world environments. The successful candidate will act as the practitioner authority for the programme's technical scope, working closely with learning designers and academic contributors to ensure the curriculum reflects current industry standards and production‑grade engineering practices.

Key Responsibilities

• 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.

Requirements

• 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.

Benefits

• 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.