Digital Twin AI Engineer: Vision, Data & Simulation

The Knowledge Transfer Network Limited

United Kingdom

Remote

GBP 70,000 - 100,000

Full time

6 days ago
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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

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

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