AI Vision System Engineer

Job Summary

As an AI Vision System Engineer, you will build production grade computer vision models that will be deployed in real factories. Your work will involve designing end to end vision pipelines that connect cameras, AI models and industrial machines. The role focuses on building reliable systems that perform consistently in real world environments. You will work closely with mechanical, electrical and automation teams to deploy AI driven inspection systems on production machines.

Requirements

• At least 1 year of hands-on experience in computer vision engineering
• Strong programming ability in Python
• Experience building image processing pipelines using OpenCV
• Ability to dive deep to understand and solve complex engineering problems independently

Responsibilities Computer Vision System Development

• Develop image processing pipelines using Python and OpenCV.
• Design robust preprocessing, feature extraction and inference workflows.
• Optimize algorithms for speed, reliability and scalability.
• Build real time image analysis systems that operate reliably on production lines.

AI Model Integration and Deployment

• Integrate trained machine learning models into production inference pipelines.
• Design decision logic that converts model outputs into actionable results.
• Ensure stable performance across different lighting conditions, parts and environments.

Software Architecture

• Develop backend services and APIs using frameworks such as Flask or FastAPI.
• Design database structures to store inspection results, logs and production data.
• Build modular software architectures that can scale across multiple machines.
• Work with engineering tools such as GitHub, Docker, CI CD pipelines, JIRA and Confluence.

Industrial Vision System Integration

• Integrate AI systems with cameras, sensors and industrial equipment.
• Deploy vision systems on edge computing platforms.
• Troubleshoot issues related to image acquisition, synchronisation and system performance.
• Collaborate with mechanical and automation engineers during machine commissioning.

Testing Continuous Improvement

• Validate system performance in real production environments.
• Investigate false detections, edge cases and system failures.
• Continuously improve model performance, inference speed and system reliability.

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