AI, Computer Vision & Digital Twin Engineer (KTP Associate)

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 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.

Anticipated interview date: 21st July 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. 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. 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 and segmentation, to solve complex, real-world challenges.