Develop Computer Vision Algorithms:
- Design, train, and optimize deep learning models for tasks such as object detection, segmentation, image classification, and pose estimation.
- Implement and refine image processing techniques for feature extraction, noise reduction, and data augmentation.
Build Scalable Models:
- Develop scalable and efficient pipelines for real-time video and image data processing.
- Collaborate with engineering teams to integrate vision models into production systems.
Data Analysis and Management:
- Collect, preprocess, and analyze large-scale datasets, including labeled and unlabeled visual data.
- Leverage techniques like supervised, unsupervised, and self-supervised learning.
Research and Innovation:
- Stay updated on the latest advancements in computer vision, deep learning, and AI.
- Experiment with state-of-the-art architectures like Transformers, GANs, or Diffusion Models to enhance model performance.
Collaborate Across Teams:
- Work closely with cross-functional teams, including data engineers, software developers, and domain experts, to understand business problems and deliver solutions.
- Present findings and insights to stakeholders using clear visualizations and reports.
Deploy and Monitor Models:
- Deploy models on cloud platforms or edge devices for real-time applications.
- Monitor model performance and implement strategies for continuous improvement.
