Role Overview: As a Data Scientist at our company, you will be an integral part of the AI team, focusing on Computer Vision and Generative AI. Your main responsibilities will include designing, training, fine-tuning, and deploying advanced models for various image understanding tasks. You will work on solutions for object detection, segmentation, depth estimation, image generation, and more. Additionally, you will collaborate closely with the engineering teams to implement and optimize AI models for production environments. Key Responsibilities: - Design, train, fine-tune, and deploy computer vision and generative AI models. - Develop solutions for object detection, segmentation, depth estimation, image inpainting, and virtual staging applications. - Build and optimize end-to-end pipelines for image understanding and generation tasks. - Evaluate model performance and implement improvements based on metrics. - Create and maintain data annotation, training, validation, and testing workflows. - Collaborate with engineering teams to productionize AI models and services. - Research and implement the latest advancements in computer vision and diffusion models. - Optimize models for inference speed, memory consumption, and scalability. - Develop robust APIs and model-serving solutions for production environments. Qualification Required: - 3+ years of hands-on experience in Machine Learning, Deep Learning, or Computer Vision. - Proven experience in developing and deploying production-grade AI solutions. - Strong understanding of CNNs, Transformers, Vision Transformers (ViTs), and Attention Mechanisms. - Expertise in PyTorch and model training, fine-tuning, and hyperparameter optimization. - Advanced Python programming skills and experience with FastAPI or similar frameworks. - Familiarity with Docker, Linux environments, Git, and collaborative development workflows. - Experience with cloud platforms such as AWS, GCP, Azure, or RunPod. - Proficiency in data handling including dataset preparation, augmentation, annotation, and quality control. - Knowledge of CVAT, Label Studio, Roboflow, or similar data annotation tools. Additional Company Details: The company values strong problem-solving and analytical skills, independent research capabilities, experience in taking models from proof-of-concept to production, and excellent communication and documentation skills. Role Overview: As a Data Scientist at our company, you will be an integral part of the AI team, focusing on Computer Vision and Generative AI. Your main responsibilities will include designing, training, fine-tuning, and deploying advanced models for various image understanding tasks. You will work on solutions for object detection, segmentation, depth estimation, image generation, and more. Additionally, you will collaborate closely with the engineering teams to implement and optimize AI models for production environments. Key Responsibilities: - Design, train, fine-tune, and deploy computer vision and generative AI models. - Develop solutions for object detection, segmentation, depth estimation, image inpainting, and virtual staging applications. - Build and optimize end-to-end pipelines for image understanding and generation tasks. - Evaluate model performance and implement improvements based on metrics. - Create and maintain data annotation, training, validation, and testing workflows. - Collaborate with engineering teams to productionize AI models and services. - Research and implement the latest advancements in computer vision and diffusion models. - Optimize models for inference speed, memory consumption, and scalability. - Develop robust APIs and model-serving solutions for production environments. Qualification Required: - 3+ years of hands-on experience in Machine Learning, Deep Learning, or Computer Vision. - Proven experience in developing and deploying production-grade AI solutions. - Strong understanding of CNNs, Transformers, Vision Transformers (ViTs), and Attention Mechanisms. - Expertise in PyTorch and model training, fine-tuning, and hyperparameter optimization. - Advanced Python programming skills and experience with FastAPI or similar frameworks. - Familiarity with Docker, Linux environments, Git, and collaborative development workflows. - Experience with cloud platforms such as AWS, GCP, Azure, or RunPod. - Proficiency in data handling including dataset preparation, augmentation, annotation, and quality control. - Knowledge of CVAT, Label Studio, Roboflow, or similar data annotation tools. Additional Company Details: The company values strong problem-solving and analytical skills, independent research capabilities, experience in taking models from proof-of-concept to production, and excellent communication and documentation skills.