Computer Vision Engineer

1–3 years hands-on experience in computer vision or deep learning roles

Implement and fine-tune modern detection/classification architectures like YOLOv8, Faster/Mask RCNN, EfficientNet, ConvNeXt, and Vision Transformers.

Conduct ablation studies, hyperparameter tuning, and model optimization experiments (e.g., pruning, quantization, distillation). Data Pipeline

Define labeling guidelines, manage annotation QA loops, and handle class imbalance strategies like re-sampling or focal loss.

Build data loaders and augmentation pipelines using libraries such as Albumentations or TorchVision, tailored to challenging industrial imagery. Evaluation & QA

Design reproducible experiments with clear metric dashboards (mAP, F1 score, PR curves).

Perform error analysis and model debugging to uncover edge-case failure modes. Deployment

Package models for deployment on cloud services (e.g., Azure, AWS).

Integrate models into production workflows using REST APIs, Docker, and CI/CD pipelines. Collaboration