We are seeking an AI/ML Engineer with strong expertise in Computer Vision (CV) to build intelligent systems that can interpret architectural drawings in DWG format. The role emphasizes designing and training computer vision pipelines — from classical CV methods to state-of-the-art deep learning models — to extract geometry, text, symbols, and structural information from technical drawings. While CAD format familiarity is helpful, deep CV expertise is the primary requirement.

• Develop and optimize computer vision models (classical + deep learning) for entity detection, segmentation, symbol recognition, and annotation extraction from architectural drawings.
• Apply classical CV techniques (e.g., edge detection, contour analysis, Hough transform, morphological operations) alongside deep learning models to solve vector and raster understanding tasks.
• Design and train deep learning models (e.g., CNNs, Mask R-CNN, U-Net, YOLO, DETR, Vision Transformers) for detection and segmentation of CAD drawing elements.
• Implement OCR pipelines for text and dimension extraction in drawings.
• Build robust data pipelines: preprocessing DWG files, rasterization/vectorization, augmentation, and dataset creation for supervised training.
• Benchmark, evaluate, and continuously improve model accuracy, robustness, and efficiency.
• Collaborate with cross-functional teams to integrate vision models into design automation and CAD/BIM workflows.

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EDUCATION & BACKGROUND

• Bachelor’s, Master’s, or PhD in Computer Science, Artificial Intelligence, Computer Vision, or related fields.
• Strong foundation in mathematics, geometry, and image processing.

COMPUTER VISION EXPERTISE (PRIORITY)

• 3+ years of hands-on experience building CV pipelines and production-ready ML models.
• Proven track record with classical CV algorithms (OpenCV, scikit-image): contour/edge detection, shape matching, geometric transformations, Hough transform, morphological filtering.
• Strong experience training and deploying deep learning CV models: CNNs, U-Net, Mask R-CNN, Faster R-CNN, YOLO, DETR, Vision Transformers, SAM, etc.
• Experience with OCR (e.g., Tesseract, deep-learning-based text recognition).
• Practical experience in combining classical CV with deep learning for hybrid solutions.

TECHNICAL SKILLS

• Proficiency in Python and ML frameworks (PyTorch, TensorFlow).
• Strong engineering practices: Git, CI/CD, testing, Docker, and scalable inference deployment.
• Familiarity with vector graphics, CAD data formats (DWG/DXF), and computational geometry is a plus, but not mandatory.

PREFERRED SKILLS

• Knowledge of geometric deep learning or graph-based approaches for structured vector data.
• Experience with annotation tools, dataset creation, and augmentation for CV tasks.
• Familiarity with AEC (Architecture, Engineering, Construction) workflows is an advantage.