Computer Vision - Data Scientist
RemoteUnited States
Job Summary
Design, develop, and deploy enterprise-scale Computer Vision solutions using state-of-the-art Deep Learning architectures like CNNs and Vision Transformers. Build and optimize models for image classification, object detection, segmentation, OCR, tracking, and video analytics while managing end-to-end AI pipelines from data preparation to monitoring. Analyze large-scale multimodal datasets to generate actionable insights and benchmark model performance against industry-standard metrics. Collaborate with engineering, product, and business teams to translate requirements into scalable AI applications across cloud and edge environments, implementing MLOps best practices for continuous improvement. Work within a 1:30 PM IST to 10:30 PM IST schedule to drive innovation in the Data Science Practice.
Required Qualifications
- Ph.D. in Computer Science, Artificial Intelligence, Computer Vision, Machine Learning, Data Science, Applied Mathematics, or a related field
- 2+ Years of hands-on experience building and deploying Computer Vision solutions in production environments
- Strong understanding of Computer Vision, Deep Learning, Machine Learning, and Image Processing techniques
- Experience with object detection, image classification, segmentation, OCR, pose estimation, and video analytics
- Hands-on experience with PyTorch, TensorFlow, Keras, OpenCV, or similar frameworks
- Strong knowledge of modern Computer Vision architectures such as YOLO, Faster R-CNN, Mask R-CNN, ResNet, EfficientNet, DETR, and Vision Transformers
- Proficiency in Python and SQL
- Experience with cloud platforms such as AWS, Azure, or GCP
- Familiarity with Docker, Kubernetes, CI/CD pipelines, and MLOps tools
- Experience with model evaluation, optimization, and deployment of scalable AI systems
- Strong analytical, problem-solving, and communication skills
- Ability to collaborate effectively with cross-functional teams and stakeholders
Desired Qualifications
- Experience with vision-language models and multimodal AI frameworks such as CLIP and BLIP
- Experience deploying real-time or edge AI applications
- Familiarity with annotation tools such as CVAT, Labelbox, or Supervisely
- Domain experience in Healthcare, Manufacturing, Retail, Automotive
- Experience working with US or European clients
- Leadership or mentoring experience in AI/ML projects
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