GenAI Machine Learning

We are seeking a skilled and motivated GenAI (Generative AI) Machine Learning Engineer to join our team. In this role, you will be responsible for developing, implementing, and optimizing generative machine learning models. You will work on cutting-edge AI technologies such as GANs (Generative Adversarial Networks), transformers, and large-scale generative models, collaborating with cross-functional teams to build advanced AI solutions. Key Responsibilities: • Model Development: Design, develop, and fine-tune advanced generative AI models, including but not limited to GANs, VAEs, and transformers, to solve complex problems across various domains. • Algorithm Optimization: Implement and optimize machine learning algorithms for training large models efficiently, focusing on performance, scalability, and real-time inference. • Data Management: Work with large datasets, perform data cleaning, preprocessing, and feature engineering for training generative models. • Model Evaluation & Testing: Conduct rigorous testing and evaluation of models, ensuring their robustness, accuracy, and ability to generalize across unseen data. • Collaboration: Collaborate with data scientists, software engineers, and product teams to integrate generative models into real-world applications. • Research & Innovation: Stay up-to-date with the latest research in generative AI and machine learning, applying new techniques and innovations to improve model performance. • Documentation: Write detailed reports and document code, model design, and best practices for reproducibility and knowledge sharing. Required Qualifications: • Education: Bachelor’s, Master’s, or Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, or a related field. • Experience: o Proven experience in building and deploying machine learning models, especially generative models (GANs, transformers, VAEs, etc.). o Strong understanding of deep learning frameworks (e.g., TensorFlow, PyTorch, JAX). o Experience with cloud platforms such as AWS, GCP, or Azure for deploying AI models at scale. o Familiarity with distributed computing techniques and parallel processing.