Job Description Summary

Job Description

Essential Functions

• AI/Digital Inspection Industrialization: Support the implementation and adoption of advanced non-destructive testing (NDT) and automated visual inspection systems, working as a key member of a cross-functional team to transition from manual checks to AI-driven defect detection.
• Computer Vision & Deep Learning: Assist in the deployment of machine learning models to identify blade surface defects, composite layup irregularities, and structural anomalies by gathering and cleaning data under the guidance of senior technical leads.
• Automated Metrology Integration: Leverage high-precision scanning, LiDAR, and photogrammetry to generate documentation for blade production, ensuring that results align with predefined design specifications.
• Closed-Loop Quality Systems: Input inspection data into the manufacturing execution system (MES) as directed, assisting in the monitoring of process control and reporting deviations from standard settings.
• Standardization of Quality Metrics: Follow global digital inspection standards and control plans, assisting in the validation of AI model performance and consistency across the production line.
• Cross-Functional Strategy: Act as a technical contact between Data Science/R&D and Shop Floor Operations, relaying information and assisting in the communication of digital inspection roadmaps.

Skills & Mindset

• Digital Literacy: Working knowledge of Computer Vision and Deep Learning frameworks, with a focus on assisting in the integration of edge computing into manufacturing hardware.
• Change Agent: Ability to adapt to new methods, supporting the transition from traditional, manual quality methods to automated, data-driven inspection by demonstrating the value of "AI-assisted" tools to the shop floor.
• Quality First: A mindset focused on "Zero Defect" manufacturing through diligent participation in automated monitoring and reporting processes.
• Global Collaboration: Ability to collaborate with remote data science teams, providing feedback on model performance to help refine accuracy and maintain operational standards across manufacturing sites.

Qualifications & Experience

• Education: Bachelor’s degree in Engineering (Manufacturing, Mechanical, or Electrical), Computer Science, or Robotics.
• Experience: Demonstrated track record in manufacturing process engineering, specifically in the implementation of automated inspection or quality control systems.
• Technical Core: Solid foundation in machine vision, imaging hardware (cameras/sensors), data handling, and composite manufacturing processes.
• Travel: Willingness to travel (approx. 20%) to support pilot trials, digital system commissioning, and factory start-ups.

Desired Characteristics

• Experience with automated defect recognition (ADR) software or industrial machine vision platforms (e.g., Cognex, Keyence, or custom Python-based solutions).
• Understanding of large-scale composite inspection challenges (blades, aerospace, automotive).
• Strong analytical skills with a focus on statistical process control (SPC) and machine learning model validation.
• Effectively influences across a wide range of stakeholders to build consensus on digital quality standards.

Additional Information