Senior Computer Vision Engineer (Industrial AI)
At a glance
Senior Machine Learning Engineering role at Fa Eocc Saasfaprod1. Suzhou · full-time.
Pay not stated Machine Learning Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Develop deep-learning vision models for defect detection, classification and process control, then prepare them for production use
- Run image data from acquisition and annotation through preprocessing and dataset upkeep, accounting for factory variability
- Integrate vision models with production lines and automation, validating cameras, optics, lighting and edge-device setups
- Monitor accuracy, precision, recall, latency and uptime in production, investigating issues and tuning system performance
- Coordinate deployments with production, quality, IT/OT and engineering teams, and help extend solutions across plants
What you bring
- You have experience developing and optimizing computer vision and deep-learning models for industrial applications
- You can take vision models from early proofs of concept into production systems and support their operation
Who this fits
This senior individual-contributor role suits an engineer focused on industrial computer vision, data pipelines and production deployment. The role is based in Suzhou, China.
From the employer
1. Computer Vision Model Development
• Design, develop, and optimize advanced computer vision and deep learning models for industrial applications such as defect detection, classification, and process control.
• Apply state-of-the-art algorithms and continuously improve model performance through validation, tuning, and retraining.
• Drive the transition of models from proof-of-concept to production-ready solutions. 2. Data Engineering & Pipeline Management
• Own the end-to-end data lifecycle, including acquisition, annotation, preprocessing, and dataset management.
• Ensure data robustness and quality under real industrial conditions (e.g., lighting, materials, positioning variability).
• Collaborate on scalable data and deployment pipelines supporting efficient training and inference. 3. Industrial Deployment & System Integration
• Deploy computer vision models into real-time industrial systems integrated with production lines and automation equipment.
• Define and validate optimal hardware configurations (cameras, optics, lighting, edge devices).
• Ensure deployed systems meet industrial standards for performance, reliability, and maintainability. 4. Performance Monitoring & Continuous Optimization
• Define and track key performance indicators (e.g., accuracy, precision, recall, latency, uptime).
• Analyze system behavior in production and implement improvements to enhance robustness and efficiency.
• Troubleshoot operational issues and optimize performance under real-world constraints. 5. Cross-functional Collaboration & Scaling
• Work closely with production, quality, IT/OT, and engineering teams to ensure successful deployment and adoption.
• Support scaling of computer vision solutions across plants and business units.
• Engage with internal and external stakeholders (local institutions, Suppliers) to accelerate industrial AI implementation.