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

The Knowledge Transfer Network Limited; United Kingdom; Remote; GBP 70,000 - 100,000; Full time

Job summary

Saith is seeking a talented individual to deliver end-to-end AI and data-driven solutions in real-world engineering projects, focusing on Digital Twin and simulation-based systems using BIM, LiDAR, and sensor data. You will build scalable data pipelines in Python and SQL, apply ML and computer vision techniques for predictive maintenance and anomaly detection, and work with cross-functional teams.

This role offers opportunities to impact asset performance and operational efficiency while

Qualifications

• Experience delivering end-to-end AI and data-driven solutions in real-world operations.
• Experience building scalable data pipelines using Python and SQL.
• Hands-on experience applying machine learning and computer vision techniques (detection, classification, segmentation, anomaly detection).

Responsibilities

• Translate stakeholder needs into technical system designs through workshops with engineers and clients.
• Design and maintain data pipelines for spatial, temporal, and operational data.
• Develop ML and CV models for recognition, anomaly detection, and predictive maintenance.
• Lead deployment, testing, and evaluation of AI-enabled Digital Twin solutions in live projects.
• Create dashboards and decision-support tools to communicate insights.

Skills

AI solutions

Data pipelines

Python

SQL

Machine learning

Computer vision

Communication

Job description

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.

Duties & Responsibilities

• Translate stakeholder and operational needs into technical system designs through co-design workshops and engagement with engineers, clients, industry partners, and academics.
• Ensure solutions align with client requirements and deliver measurable operational and commercial value.
• Design and maintain data pipelines and infrastructure for ingesting, processing, and managing spatial, temporal, and operational data.
• Process and integrate BIM, LiDAR, and point-cloud data into consistent and usable Digital Twin environments.
• Develop and apply machine learning and computer vision models for component recognition, anomaly detection, compliance monitoring, and predictive maintenance.
• Design and implement an AI-enabled Digital Twin system integrating multi-source data for infrastructure asset management.
  Work closely with design, construction, and operational teams to integrate and validate solutions in live projects and real-world environments.
• Develop simulation and risk analysis tools to support inspection, maintenance planning, and operational decision-making.
• Create dashboards and decision-support tools to visualise data and communicate actionable insights.
• Lead pilot deployment, system testing, and performance evaluation in real-world environments.
• Drive adoption of AI and Digital Twin technologies within Saith through training, documentation, and embedding of new workflows across the business.

This is a unique opportunity for an ambitious candidate to contribute to the development of scalable digital solutions, supporting improved asset performance, operational efficiency, and data-driven decision-making across infrastructure and utility sectors. You will also have the opportunity to contribute to the long-term development of AI and Digital Twin capability within the organisation, supporting future growth and innovation.

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.

This is an exciting career-development opportunity to deliver a high-impact project, working with academic experts and industry professionals, and gaining hands-on experience in deploying advanced AI solutions within a real-world engineering consultancy environment.

About the business

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.