- Robust 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
- Explicit communicator of technical trade-offs to both technical and non-technical stakeholders

📌 AI ML-Computer Vision (Bengaluru)
🏢 Wroots Global
📍 Bengaluru