Senior Data Scientist

As a Senior Data Scientist specializing in Computer Vision and Generative AI, your role will involve leading cutting-edge R&D efforts and model deployment in the field. You will be responsible for driving innovation in vision-language systems and diffusion-based models, from research prototypes to scalable production-grade pipelines. Key Responsibilities: - Design, train, and optimize deep learning models for tasks such as image generation, image-to-image translation, and text-to-image synthesis. - Conduct advanced R&D in Generative AI, focusing on models like Diffusion Models, Transformers, VAEs, and GANs. - Explore Vision-Language and multi-modal learning using models like CLIP, Flamingo, PixArt, and Qwen2-VL. - Implement distributed training and model parallelism techniques using PyTorch, Deep Speed, and Hugging Face Accelerate. - Lead experiments with LoRA, QLoRA, adapters, and parameter-efficient fine-tuning methods. - Develop evaluation pipelines for visual realism, garment fidelity, and human perceptual quality metrics. - Collaborate with product and engineering teams to deploy models to production using Triton, Torch Serve, or Fast-API. - Design automated ML-Ops pipelines for training, evaluation, and deployment, including CI/CD, monitoring, and retraining. - Mentor junior ML-engineers and data scientists, ensuring best practices in model reproducibility, experiment tracking, and code quality. Qualifications Required: - Hands-on experience in applied ML/AI, with a focus on Computer Vision and Generative Models. - Proficiency in tasks such as image generation, inpainting, style transfer, human parsing, and pose estimation. - Strong knowledge of architectures like U-Net, ViT, Transformer-based encoders/decoders, VAE, and GAN variants. - Expertise in distributed training, data parallelism, and GPU/TPU acceleration. - Experience with synthetic data generation, texture alignment, and dataset curation pipelines. - Proven track record of deploying production-grade ML systems using technologies like Fast-API, Triton, Torch Serve, or REST APIs. - Proficient in Python, PyTorch, OpenCV, NumPy, Hugging Face, and WandB/MLflow. As a Senior Data Scientist specializing in Computer Vision and Generative AI, your role will involve leading cutting-edge R&D efforts and model deployment in the field. You will be responsible for driving innovation in vision-language systems and diffusion-based models, from research prototypes to scalable production-grade pipelines. Key Responsibilities: - Design, train, and optimize deep learning models for tasks such as image generation, image-to-image translation, and text-to-image synthesis. - Conduct advanced R&D in Generative AI, focusing on models like Diffusion Models, Transformers, VAEs, and GANs. - Explore Vision-Language and multi-modal learning using models like CLIP, Flamingo, PixArt, and Qwen2-VL. - Implement distributed training and model parallelism techniques using PyTorch, Deep Speed, and Hugging Face Accelerate. - Lead experiments with LoRA, QLoRA, adapters, and parameter-efficient fine-tuning methods. - Develop evaluation pipelines for visual realism, garment fidelity, and human perceptual quality metrics. - Collaborate with product and engineering teams to deploy models to production using Triton, Torch Serve, or Fast-API. - Design automated ML-Ops pipelines for training, evaluation, and deployment, including CI/CD, monitoring, and retraining. - Mentor junior ML-engineers and data scientists, ensuring best practices in model reproducibility, experiment tracking, and code quality. Qualifications Required: - Hands-on experience in applied ML/AI, with a focus on Computer Vision and Generative Models. - Proficiency in tasks such as image generation, inpainting, style transfer, human parsing, and pose estimation. - Strong knowledge of architectures like U-Net, ViT, Transformer-based encoders/decoders, VAE, and GAN variants. - Expertise in distributed training, data parallelism, and GPU/TPU acceleration. - Experience with synthetic data generation, texture alignment, and dataset curation pipelines. - Proven track record of deploying production-grade ML systems using technologies like Fast-API, Triton, Torch Serve, or REST APIs. - Proficient in Python, PyTorch, OpenCV, NumPy, Hugging Face, and WandB/MLflow.