AI, Computer Vision & Digital Twin Engineer (KTP Associate)
| Company: | University of Derby |
|---|---|
| Salary: | from £36,600 a year |
| Hours: | Full-time |
| Location: | Derby, DE22 3AW |
| Working pattern: | On-site |
| Job type: | Contract |
| Posting date: | 29 Sept 2026 |
| Closing date: | 10 Oct 2026 |
Summary
At Saith, our people are central to everything we do. We are an independent engineering management consultancy delivering high-quality solutions across the energy and utility sectors. Our teams work collaboratively across project management, design, CDM, and technical assurance.
We support clients across the full project lifecycle, from early-stage studies and detailed design through to delivery and implementation. We value professionalism, accountability, collaboration, and technical excellence, and are committed to developing our people to deliver safe, practical, and commercially effective outcomes.
About the Role
University of Derby’s College of Science and Engineering in partnership with Saith Ltd are offering an exciting career-development opportunity to manage and deliver a challenging strategic Knowledge Transfer Partnership (KTP) project. Based at Saith Ltd’s premises in Hampshire, you will be employed by the University as a KTP Associate but work under the terms and conditions of the company.
You will lead the design, development, and deployment of a practical, production-ready AI-enabled Digital Twin platform to support intelligent asset management within energy and utility infrastructure. You will work at the interface of research and industry, translating advanced AI, computer vision, and data-driven methods into scalable solutions deployed within live engineering environments.
You will take ownership of the end-to-end system lifecycle, integrating multi-source data including BIM, LiDAR, point cloud, and operational datasets to develop solutions for automated inspection, anomaly detection, predictive maintenance, and intelligent decision-support. Crucially, you will ensure that solutions are commercially viable, aligned with client requirements, and deliver measurable operational value.
Working closely with design, construction, and operational teams, you will contribute directly to live engineering projects, ensuring solutions are effectively integrated, validated, and deliver real-world impact. The role will also support the adoption of AI and Digital Twin technologies within Saith through training, knowledge transfer, and integration into operational workflows.
Using your knowledge and skills to support the business and achieve specific project aims, you will receive on-going support by a Supervisor at Saith Ltd, an Academic Supervisor and Academic Lead from the University of Derby to help you deliver. A personal training and development budget will also be available for you to access, as well as receiving access to the same training opportunities and facilities as University staff.
Anticipated interview date: Friday 30th October 2026
Knowledge Transfer Partnerships
This full-time post is part-funded by the UK Government’s KTP programme. A KTP is a three-way project between a graduate, an organisation and a university. KTP is one of the UK’s largest graduate recruitment programmes and has been placing graduates on challenging, high profile projects for 50 years. In addition to a competitive salary and core KTP development training, you will have a dedicated budget of £4,000 further training and career development. To find out more about the scheme visit: ktp-uk.org/graduates
Please note by completing an application form for this role, you are giving your consent for us to share your personal data with the KTP partner.
About You
We’re looking for a talented and driven individual with experience delivering end-to-end AI and data-driven solutions in real-world operational environments, with a clear focus on achieving measurable impact. In this role, you’ll work on developing Digital Twin and simulation-based systems, leveraging multi-source data such as BIM, LiDAR, point cloud, and sensor data to support prediction, enhance decision-making, and improve operations.
You’ll bring strong experience in building and optimising scalable data pipelines using Python and SQL, managing the full data lifecycle from ingestion through to processing and validation. You’ll also have hands-on experience applying machine learning and computer vision techniques, such as detection, classification, segmentation, and anomaly detection, to solve complex, real-world challenges.
You will work both independently and as part of multidisciplinary teams, bringing a practical, solutions-focused mindset. Strong communication skills are essential, with the ability to clearly present complex technical concepts to both technical and non-technical stakeholders.
Benefits
Whilst you will be employed by University of Derby, your terms and conditions will be that of Saith Ltd.
Key Contact
For further information and informal enquiries regarding the role, please contact Dr Oluwarotimi W. Samuel, Senior Lecturer in Computer Science via o.samuel@derby.ac.uk, or Dr Mojisola Grace Asogbon, Lecturer in Data Science via m.asogbon@derby.ac.uk
For enquiries regarding your application and for sponsorship eligibility, please contact the recruitment team via recruitment@derby.ac.uk
Important Information
This role may be eligible for sponsorship by the University.
The offered salary for this role is less than the going rate for the occupation and the minimum salary threshold for the Skilled Worker route (£41,700 per annum, as of 22nd July 2025). Therefore you will only be eligible to apply for a Skilled Worker visa subject to your individual circumstances which must meet one of the following criteria set out by UKVI:
Your job is on the Immigration Skills List
You’re under 26, studying or a recent graduate, or in professional training
You have a PhD level qualification that’s relevant to your job
You have a postdoctoral position in science or higher education
You must also meet the English Language requirement.
Please note that the University will assess your individual eligibility for sponsorship at the shortlisting stage.
For more information, visit our website.
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