AI Engineering Subject Matter Expert
| Posting date: | 17 April 2026 |
|---|---|
| Salary: | £45,000 to £55,000 per year |
| Hours: | Full time |
| Closing date: | 17 May 2026 |
| Location: | Milton Keynes, Buckinghamshire |
| Remote working: | On-site only |
| Company: | GAK ENTERPRISES LTD |
| Job type: | Permanent |
| Job reference: | GAK_04_AIESME_2026_06 |
Summary
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
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