Industrial AI Engineer

Job Description:

Important Notes:

• Vision & Technology project.
• An ideal candidate should be experienced with machine learning and vision systems using PLC.
• 5 years of experience in Machine Vision & Edge AI development.
• Proficiency in Python and C++

Who we’re looking for:

• Our Client’s Manufacturing Project Innovation Center (Manufacturing Innovation) – Advanced Technology Department is looking for a passionate and highly motivated Advanced Technology Engineer

What you Must have:

• Experience with project management including writing detailed scope of work, creating schedules, managing vendors/contractors, and providing regular status updates (Y/N – text follow up)
• Bachelor’s degree in Electrical Engineering, Mechanical Engineering, Computer Science, Information Technology or related field.
• 5 years of experience in industrial machine vision and edge AI deployment.
• Proficiency in Python and C++ with strong knowledge of ML frameworks (PyTorch, TensorFlow).
• Hands-on experience with containerization (Docker) and orchestration (Kubernetes).
• Familiarity with ONNX Runtime, TensorRT, and optimization for embedded hardware.
• Experience integrating vision systems with PLCs and industrial protocols (OPC-UA, MQTT).
• Experience managing the full model lifecycle: data collection, labeling, validation, rollout, monitoring, and retraining
• Experience in areas such as object detection, classification, segmentation and familiarity with mainstream object detection and semantic/instance segmentation models.
• Familiarity with industrial cameras, lighting, and optics, including trigger-based image capture
• Experience balancing inspection accuracy with false positives vs flow-out risk in quality

Requirements: What you may bring:

• Master’s Degree in Engineering or Advanced Degree in related fields
• Academic research experience in new technology
• Project management work involving internal and external parties – 6 months or greater
• Experience deploying equipment including establishing RJ, PFMEA, and quality control plan
• Experience deploying automotive production equipment
• Experience in Robotics to include operation, teaching, maintenance, and safety
• Expertise in synthetic data generation techniques (GANs, VAEs, NeRFs, Blender) and domain randomization for model generalization.
• Experience with high-speed inline inspection systems and vision-based process control.
• Knowledge of IIoT data pipelines and messaging standards.
• Strong understanding of calibration, measurement system analysis (MSA), and quality-critical inspection requirements.

Key Competencies

• Ability to deliver production-ready AI solutions under strict timelines.
• Strong problem-solving and cross-functional collaboration skills.
• Commitment to quality, reliability, and continuous improvement in manufacturing environments

The primary responsibility of this role is:

Model Development & Training Speed

• Design and implement computer vision models for defect detection, segmentation, and classification.
• Accelerate training cycles using synthetic data, active learning, and domain randomization to cover 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 observability for latency, 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 Our Client’s ICS+, GALC, and TVIP architecture standards for plant-level connectivity and reliability.

Data Strategy & Quality Control

• Lead data collection campaigns, manage annotation workflows, and establish quality gates for model validation.
• Utilize synthetic data pipelines and augmentation techniques to improve model robustness and reduce training time.

Reliability & Sustainment

• Ensure uptime and availability targets are met through proactive monitoring, calibration (MSA), and backup/restore processes.
• Implement drift detection, audit false-out risks, and perform root cause analysis for inspection failures.
• Reporting to the Manufacturing Innovation Manager, the person in this role will support the Production Engineering Division and SOAR Group’s objective to improve manufacturing competitiveness.

What you’ll be doing:

• Develop and deploy production-grade machine learning models for industrial vision inspection systems across Our Client manufacturing lines.
• Accelerating model development and training using advanced techniques such as synthetic data generation, ensuring high accuracy and generalization, and delivering containerized software optimized for edge hardware
• Development of new technologies for PE and Manufacturing competitiveness improvement
• Lead and manage projects from concept to launch for new technology first introduction to manufacturing including creating schedules, establishing punch lists, and meeting established due dates and milestones.
• Search for innovative solutions, test them in a manufacturing setting, and develop business case justification to gain approval to purchase if trials prove successful.
• Close collaboration between both internal and external groups across Our Client including North American Manufacturing Centers ( NAMCs), Our Client Motor Corp (TMC), Our Client Technical Center (TTC) , PE support shops, IT ( One Tech) teams, automation teams & Plano divisions, to ensure quality and to integrate robust AI solutions into high-volume manufacturing environments.