Computer Vision Expert

• Strong foundation in computer vision techniques: object detection, image segmentation, classification, pose estimation, OCR, and tracking
• Experience in fine-tuning pre-trained architectures (ResNet, YOLO, ViT, DETR, SAM, CLIP), choosing the right approach based on data availability and use case
• Proficiency in deep learning frameworks PyTorch, TensorFlow — along with CV libraries like OpenCV, Detectron2, MMDetection, and Hugging Face Transformers/timm
• Solid understanding of neural network architectures for vision (CNNs, Vision Transformers, GANs, diffusion models) and ability to design or adapt them for specific problems
• Experience with transfer learning and parameter-efficient fine-tuning (LoRA, adapters) to adapt large/foundation models efficiently
• Skilled in data pipeline development — collection, annotation (CVAT, Labelbox), augmentation, and synthetic data generation
• Experience with model optimization for production: quantization, pruning, distillation, and deployment via TensorRT, ONNX, OpenVINO, or on edge devices
• Working knowledge of MLOps practices — experiment tracking (MLflow, W&B), CI/CD for ML, model versioning, and production monitoring
• Familiarity with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes) for scalable deployment
• Sensor fusion/multi-modal data experience (LiDAR, depth cameras) a plus, depending on domain

Leadership & Soft Skills

• Proven experience leading a team of CV/ML engineers — mentoring, code reviews, technical growth
• Ability to evaluate build-vs-fine-tune-vs-buy trade-offs and set realistic project roadmaps
• Track record of shipping models from prototype to production at scale
• Strong cross-functional collaboration with product, data, and engineering teams
• Stays current with CV research and emerging pre-trained models, applying them pragmatically
• Clear communicator of technical trade-offs to both technical and non-technical stakeholders