Computer Vision Engineer

We are seeking a
Computer Vision Engineer
with hands-on experience in
PyTorch
and
TensorFlow
to design, develop, and deploy deep learning–based vision systems for manufacturing automation. The ideal candidate will apply AI and image processing to improve product quality, automate inspections, and enhance process efficiency across the production floor.

Key Responsibilities

• Design and train deep learning models
  using
  PyTorch
  and/or
  TensorFlow
  for tasks such as defect detection, surface inspection, object localization, and classification.
• Build and optimize computer vision pipelines
  , including image preprocessing, data augmentation, and real-time inference.
• Integrate AI models
  with industrial systems—robotic cells, PLCs, and camera networks—for automated inspection and guidance.
• Deploy trained models
  to edge computing platforms (e.g., NVIDIA Jetson, Intel OpenVINO, or ONNX Runtime) for real-time inference.
• Collect, label, and manage datasets
  from production lines to improve model robustness and accuracy.
• Collaborate with automation, quality, and process engineering teams
  to define system requirements and deploy vision solutions in manufacturing environments.
• Monitor and maintain deployed models
  , implementing retraining and continuous improvement cycles.
• Research emerging techniques
  in computer vision and deep learning, staying current with innovations in PyTorch, TensorFlow, and model optimization.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Electrical/Mechanical Engineering, Robotics, or related field.
• 2 years of hands-on experience
  with
  PyTorch
  and/or
  TensorFlow
  for computer vision applications.
• Strong understanding of
  CNNs, Vision Transformers, object detection models
  (e.g., YOLO, Faster R-CNN, SSD), and
  segmentation networks
  (e.g., U-Net, Mask R-CNN).
• Proficiency in
  Python
  and familiarity with supporting libraries:
  OpenCV, NumPy, scikit-learn, Pandas, Matplotlib
  .
• Experience deploying and optimizing models for
  real-time inference
  in
  manufacturing or industrial environments
  .
• Understanding of
  camera calibration, lighting, optics, and image acquisition systems
  .
• Familiarity with
  version control (Git)
  ,
  Docker
  , and
  Linux environments
  .

Preferred Qualifications

• Experience with
  3D vision
  ,
  depth cameras
  , or
  LiDAR
  for dimensional inspection.
• Experience integrating models with
  C
  or
  industrial vision platforms
  (Cognex, Keyence, Basler).
• Familiarity with
  ONNX
  ,
  TensorRT
  , or
  OpenVINO
  for model conversion and acceleration.
• Knowledge of
  MLOps
  workflows for model deployment and monitoring.
• Experience in
  Industry 4.0
  ,
  predictive maintenance
  , or
  smart manufacturing
  applications.