Job Description Summary

Be at the forefront of the energy transition. As an Advanced Manufacturing Engineer (AME) for Blades, you won’t just manage factory processes—you will engineer the future of sustainable energy. You are a hands-on technical leader responsible for translating breakthrough manufacturing concepts into robust, industrialized realities. You will partner across the global GE Vernova organization to drive technologies that optimize SQDC (Safety, Quality, Delivery, Cost) and shape the next generation of blade production.

Job Description

Essential Roles & Responsibilities:

• AI/Digital Inspection Industrialization: Design, specify, and implement advanced non-destructive testing (NDT) and automated visual inspection systems to transition from manual checks to AI-driven defect detection.
• Computer Vision & Deep Learning: Lead the deployment of machine learning models to identify blade surface defects, composite layup irregularities, and structural anomalies in real-time.
• Automated Metrology Integration: Leverage high-precision scanning, LiDAR, and photogrammetry to create a digital "birth certificate" for every blade, ensuring strict adherence to design specifications.
• Closed-Loop Quality Systems: Integrate AI inspection output directly into the manufacturing execution system (MES) to enable predictive process control and automatic process adjustments.
• Standardization of Quality Metrics: Deploy global digital inspection standards and control plans, ensuring that AI models are validated and consistent across the global supply chain.
• Cross-Functional Strategy: Act as the technical lead between Data Science/R&D and Shop Floor Operations to align stakeholders on digital inspection roadmaps and change management.

Skills & Mindset

• Digital Literacy: Mastery of Computer Vision, Deep Learning frameworks (e.g., TensorFlow, PyTorch), and the integration of edge computing into manufacturing hardware.
• Change Agent: Ability to mentor teams transitioning from traditional, manual quality methods to automated, data-driven inspection, while managing the cultural shift toward "AI-assisted" manufacturing.
• Quality First: A mindset focused on "Zero Defect" manufacturing through proactive, automated monitoring rather than reactive inspection.
• Global Collaboration: Experience working with remote data science teams to refine model accuracy and operationalize digital solutions across multiple 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