AI Vision Engineer for Industrial Quality Inspection (KTP)

The Methodist Church in Britain; East Midlands; On-site; GBP 34,000 - 42,000; Full time

Job summary

Eminox partners with the University of Lincoln for a 24-month Knowledge Transfer Partnership to develop an AI-enabled vision system that detects defects in Diesel Particulate Filter units and predicts their performance in production. The associate leads the technical build and deploys it in live manufacturing environments.

Eligible candidates hold a Master’s in engineering or computing and have hands-on experience in machine learning, computer vision and data analytics, with strong collaboration

Qualifications

• Master's degree in engineering/computing discipline is essential.
• Experience in ML, CV and/or data analytics.
• Experimental work and quality-inspection techniques or systems.
• Researching and developing new or improved engineering systems or methods.

Responsibilities

• Develop machine-vision methods for defect detection and classification.
• Design and train AI models, including computer vision and deep-learning models.
• Capture multi-modal industrial datasets and establish data pipelines.
• Develop intelligent methods to predict downstream performance of DPF units.
• Integrate AI algorithms with manufacturing processes.
• Build prototypes and conduct system-integrated testing.
• Validate and optimise the system under industrial conditions.
• Lead technical milestones and communicate results.
• Translate research approaches into commercially viable solutions.
• Contribute to research outputs, reports, and publications.

Skills

Machine learning

Computer vision

Data analytics

Python

Mathematics

Critical thinking

Project management

Independent working

Education

Master's degree in mechanical engineering, manufacturing engineering, robotics, AI, computer science, data science or closely related discipline

Tools

OpenCV

PyTorch

TensorFlow

MATLAB

Job description

Eminox partners with the University of Lincoln for a 24-month Knowledge Transfer Partnership to develop an AI-enabled vision system that detects defects in Diesel Particulate Filter units and predicts their performance in production. The associate leads the technical build and deploys it in live manufacturing environments.

Eligible candidates hold a Master’s in engineering or computing and have hands-on experience in machine learning, computer vision and data analytics, with strong collaboration