Hire computer vision engineers who ship detection that holds up on real cameras

• You have thousands of hours of camera footage and need real-time object detection running on an edge box like a Jetson, not a slow cloud round trip.
• Off-the-shelf OCR APIs keep mangling your specific documents, like handwritten forms, receipts, or IDs, so you need a custom extraction pipeline.
• You are training segmentation models on proprietary imagery, such as medical scans or satellite tiles, that you cannot ship to a third-party vision API.
• You want visual search, defect detection, or image moderation added to a product that already handles a large volume of images.

Every engineer we put forward is screened by a senior Grape5 engineer before you meet them. For computer vision engineers, we look specifically at:

• Architecture fit: whether they choose the right model for the constraint, like a YOLO family detector for real-time video versus a two-stage Faster R-CNN or Mask R-CNN when accuracy and segmentation matter, and can defend the mAP versus latency tradeoff.
• Data judgment: how they handle class imbalance, mislabeled or thin training sets, and augmentation with tools like Albumentations, plus whether they evaluate with IoU and mAP instead of leaning on raw accuracy.
• Deployment reality: exporting to ONNX or TensorRT, quantizing for Jetson or mobile targets like CoreML and TFLite, and holding inference latency inside a per-frame budget.
• Failure-mode instinct: catching domain shift between training data and real cameras, plus lighting, occlusion, small objects, and false positives, before they reach production.
• Classic CV fundamentals: camera calibration, color spaces, and morphological operations, and knowing when plain OpenCV beats reaching for a neural net.