Computer Vision Engineer
Summary
Designs, deploys, and maintains computer vision systems for automated inspection, defect detection, and quality assurance at Continental's manufacturing operations in Hefei, China. Day-to-day work spans integrating industrial cameras, lighting, PLCs, and edge-computing platforms, and building/monitoring vision models using Python and AI/vision frameworks.
• Design, develop, deploy, and maintain computer vision solutions for automated inspection, defect detection, quality assurance, process monitoring, and manufacturing intelligence.
• Integrate vision applications with industrial cameras, lighting, controls, manufacturing execution environments, databases, and edge-computing platforms.
• Lead or support validation, commissioning, production rollout, user enablement, and operational support for plant-wide vision use cases.
• Monitor production performance and improve model robustness under changing products, equipment conditions, lighting, and process variation.
• Diagnose image-acquisition, camera-calibration, equipment-drift, and model-performance issues and drive sustainable corrective actions.
• Identify and prioritize new industrial vision opportunities, translating manufacturing pain points into deployable use cases with measurable operational impact.
• Evaluate emerging technologies such as deep-learning vision, multimodal AI, foundation vision models, and industrial vision platforms.
• Apply appropriate data, cybersecurity, model-risk, and responsible-AI controls throughout the solution lifecycle.
Computer vision and image processing, including model development, validation, performance measurement, and deployment.
• Python and relevant AI / vision frameworks; practical software deployment skills. Additional industrial programming capability is advantageous. Confidential | Prepared for compensation benchmarking purposes only
• Industrial cameras, optics, lighting, calibration, sensors, and edge-computing concepts.
• Industrial integration concepts, including PLC / controls, manufacturing systems, databases, APIs, and common connectivity protocols.
• Data analysis and visualization; structured problem solving using quality and root-cause methods.
• Project management, stakeholder communication, documentation, and change adoption in an operations environment.
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