Generative AI Engineer

As a Generative AI Developer at our company, you will be responsible for building cutting-edge generative models to create innovative, human-like content across various domains such as natural language generation, image generation, and more. Your expertise in machine learning, deep learning, and natural language processing will be crucial for developing AI systems that utilize generative models like GPT, GANs, VAEs, and other neural networks. **Key Responsibilities:** - Develop and fine-tune generative models using techniques like GANs, VAEs, Transformers, and Diffusion models. - Work on tasks including text generation, image generation, music/sound generation, video creation, and other creative AI applications. - Design, build, 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. - Research and stay updated on the latest trends, papers, and breakthroughs in generative AI. - Collaborate with other teams to integrate models into production systems. - Work on data preprocessing, augmentation, and designing pipelines to ensure high-quality input for model training. - Document your code, process, and model outputs for team-wide visibility and knowledge sharing. **Qualifications 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, especially 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. - Experience working with neural networks, unsupervised learning, and NLP. - Strong programming skills in Python, including deep learning libraries. - Familiarity with cloud platforms (AWS, GCP, or Azure) for model training and deployment. - Strong mathematical and statistical knowledge, particularly 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. - Familiarity with AI ethics, bias mitigation, and interpretability in generative models.,