Generative AI Engineer

Role Overview: As a skilled Generative AI Developer, you will be a valuable member of the team contributing to the advancement of cutting-edge generative models. Your primary focus will be on crafting AI systems capable of generating innovative and human-like content spanning various domains such as natural language, images, videos, and music synthesis. Key Responsibilities: - Develop and refine generative models utilizing techniques such as GANs, VAEs, Transformers, and Diffusion models. - Work on tasks involving text generation, image generation, music/sound generation, video creation, and other creative AI applications. - Design, construct, and deploy models using frameworks like TensorFlow, PyTorch, Hugging Face, OpenAI's GPT, or similar tools. - Optimize AI models for performance, accuracy, and scalability in real-world production environments. - Stay updated on the latest trends, research papers, and breakthroughs in the field of generative AI. - Collaborate with cross-functional teams to integrate models into production systems and ensure seamless operations. - Perform data preprocessing, augmentation, and design pipelines to enhance model training input quality. - Document code, processes, and model outputs for team-wide visibility and knowledge sharing. Qualification Required: - Bachelor's or Master's degree in Computer Science or related fields. - 3-4 years of hands-on experience in developing Generative AI solutions. - Strong understanding of deep learning algorithms, particularly in generative models like GANs, VAEs, Diffusion models, or large-scale language models like GPT. - Hands-on experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, or Hugging Face. - Proficient programming skills in Python, including deep learning libraries like TensorFlow, Keras, and PyTorch. - Familiarity with cloud platforms (AWS, GCP, or Azure) for model training and deployment. - Solid mathematical and statistical knowledge, especially in probability theory, linear algebra, and optimization. - Experience in large-scale model training, fine-tuning, or distributed computing. - Knowledge of reinforcement learning and self-supervised learning. - Understanding of AI ethics, bias mitigation, and interpretability in generative models.,