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Vision Language Data Scientist
2 months ago
Expectations & Responsibilities: Proactive attitude towards quick adaptability in ongoing tasks. Develop contrastive, self-supervised, semi-supervised and natural language based learning techniques to boost the performance of computer vision tasks in retail, surveillance, CPG, manufacturing and automation, and extend their functionalities. Zero-shot and few-shot learning, implementing them to address in-house use cases effectively. Collaborate on vision language integration, employing cutting-edge vision/graphics and language-based prompt tuning, demonstrate proficiency in open-vocabulary classification, object detection, segmentation and image retrieval tasks. Follow up with state-of-the-art research in unimodal generative techniques in computer vision- encompassing adversarial networks, cross-attention mechanisms, and latent diffusion modelling based approaches. Demonstrate expertise in fundamental computer vision tasks, including object detection, segmentation, pose estimation, image captioning, and image/video understanding. Hands-on experience with PyTorch, TensorFlow for prototyping and deploying models. Collaborate with cross-functional teams to integrate R&D outcomes into practical solutions. Qualifications & Expertise: Education: Qualification and academic background is not a barrier for the right candidate with proven skills. Master's or Ph.D. (completed / degree awaited) in application of Computer Vision, Artificial Intelligence with the aforementioned skills, and a strong inclination for industrial research application will be preferred. Work Experience: Prior experience in working with the aforementioned technologies is indispensable. Your previous work experience should preferably be in CV/NLP & DL domain, but not mandatory. Vision Expertise: Demonstrated expertise in fundamental computer vision tasks, like object detection, segmentation, pose estimation, image captioning and image/video understanding. Technical Skills: Proficiency in Python and C++ programming is essential. Strong knowledge of PyTorch is required for model development. Exposure to Langchain will be preferred. Problem-Solving: Strong analytical and problem-solving skills to tackle complex challenges in AI research and development. Communication: Excellent communication skills, both written and verbal, to articulate research findings and collaborate effectively with cross-functional teams, prepare proposals for client.