Job Description:

- 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.