Artificial Intelligence/Machine Learning Engineer

As an AI/ML Developer at our company, you will be responsible for developing cutting-edge Generative AI models. Your role will involve working on AI systems that can generate innovative, human-like content across various domains such as natural language generation, image generation, and video or music synthesis. Key Responsibilities: - Develop and fine-tune generative models using techniques like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Transformers, Diffusion models, etc. - Work on tasks including text generation, image generation, music or 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. - 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 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 with neural networks, unsupervised learning, and natural language processing (NLP). - Strong programming skills in Python, including deep learning libraries (e.g., TensorFlow, Keras, PyTorch). - 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 (RL) and self-supervised learning. - Familiarity with AI ethics, bias mitigation, and interpretability in generative models. As an AI/ML Developer at our company, you will be responsible for developing cutting-edge Generative AI models. Your role will involve working on AI systems that can generate innovative, human-like content across various domains such as natural language generation, image generation, and video or music synthesis. Key Responsibilities: - Develop and fine-tune generative models using techniques like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Transformers, Diffusion models, etc. - Work on tasks including text generation, image generation, music or 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. - 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 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 with neural networks, unsupervised learning, and natural language processing (NLP). - Strong programming skills in Python, including deep learning libraries (e.g., TensorFlow, Keras, PyTorch). - 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 (RL) and self-supervised learning. - Familiarity with AI ethics, bias mitigation, and interpretability in generative models.