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Advanced Technology Engineer - Vision & Edge AI Role Overview

The Advanced Technology Engineer will develop and deploy AI-powered machine vision systems for defect detection and quality inspection in high-volume manufacturing environments.

The primary focus of this role is building production-ready computer vision models, optimizing them for real-time edge hardware, and integrating them into manufacturing systems.

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Primary Responsibilities Model Development & Training Acceleration

• Design and implement computer vision models for defect detection, segmentation, and classification.

• Accelerate training cycles using synthetic data, active learning, and domain randomization to address rare defects and specification variance.

Production Deployment

• Package models and services using Docker and manage deployments through Kubernetes or equivalent orchestration tools.

• Implement version control, rollback strategies, and monitoring for latency, model drift, and false-positive/false-negative metrics.

Edge Optimization

• Optimize inference for edge and embedded hardware (e.g., NVIDIA Jetson, Client accelerators) to meet strict real-time latency requirements for moving-line inspection.

• Ensure consistent performance under varying lighting, optics, and surface conditions.

Integration with Manufacturing Systems

• Integrate vision systems with PLCs, encoders, triggers, and industrial networks using OPC-UA, MQTT, and REST protocols.

• Align deployments with plant-level architecture and connectivity standards to ensure reliability and scalability.

Data Strategy & Quality Control

• Lead data collection campaigns and manage annotation workflows.

• Establish quality gates for model validation.

• Utilize synthetic data pipelines and augmentation techniques to improve robustness and reduce training time.

Reliability & Sustainment

• Ensure uptime and availability targets through proactive monitoring, calibration (MSA), and backup/restore processes.

• Implement drift detection, audit false-out risks, and perform root cause analysis for inspection failures.

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What You'll Be Doing

• Develop and deploy production-grade machine learning models for industrial vision inspection systems.

• Accelerate model development using synthetic data and advanced AI techniques.

• Deliver containerized software optimized for edge hardware.

• Lead projects from concept through launch, including scheduling, milestone tracking, and cross-functional coordination.

• Evaluate new technologies in manufacturing environments and build business cases for adoption.

• Collaborate with internal engineering, IT, automation, and production teams to integrate robust AI solutions into high-volume manufacturing.

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Required Qualifications

• Bachelor's degree in Electrical Engineering, Mechanical Engineering, Computer Science, IT, or related field.

• 5+ years of experience in industrial machine vision and edge AI deployment.

• Strong proficiency in Python and C++.

• Experience with ML frameworks (PyTorch, TensorFlow).

• Hands-on experience with Docker and Kubernetes.

• Familiarity with ONNX Runtime, TensorRT, and embedded optimization.

• Experience integrating vision systems with PLCs and industrial protocols (OPC-UA, MQTT).

• Experience managing the full AI lifecycle: data collection, labeling, validation, rollout, monitoring, and retraining.

• Knowledge of object detection, classification, and segmentation models.

• Experience with industrial cameras, lighting, and trigger-based image capture.

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Preferred Qualifications

• Master's degree or advanced engineering degree.

• Experience deploying automotive or high-volume production equipment.

• Robotics experience (operation, teaching, maintenance, safety).

• Expertise in synthetic data generation (GANs, VAEs, NeRFs, Blender) and domain randomization.

• Experience with high-speed inline inspection systems and IIoT data pipelines.

• Strong understanding of calibration, MSA, PFMEA, and quality-critical inspection requirements.

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Key Competencies

• Ability to deliver production-ready AI solutions under strict timelines.

• Strong cross-functional collaboration and project leadership skills.

• Commitment to quality, reliability, and continuous improvement in manufacturing environments.