Integration and Collaboration: Collaborate with cross-functional teams to integrate generative AI solutions into existing workflow systems. Document and present complex findings and learnings in a clear and concise manner to a non-technical audience.
Research and Innovation: Stay up-to-date on the latest advancements in generative AI technologies and methodologies such as Retrieval Augmented Generation (RAG), Vector Databases, frameworks such as langchain, etc.
Algorithm Development: Design and develop algorithms for generative models using deep learning techniques, Natural Language Processing (NLP), Retrieval Augmented Generation (RAG) etc.
Model Architecture Design: Create and fine-tune generative neural network architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based models.
Data Management: Clean, label, and augment data for training and improving AI models.
AI Alignment and Testing: Perform rigorous testing and validation of generative AI models to ensure their robustness and reliability. Address ethical considerations related to generative AI, such as bias, fairness, and transparency.