Generative Ai Engineer
Current- Large Language Model (LLM) Development: Developed and fine-tuned large language models (e.g., GPT-3, T5) for various applications, focusing on text generation, summarization, and conversational AI.
- Generative AI Model Development: Develop and train generative AI models using techniques such as generative adversarial networks (GANs), variational autoencoders (VAEs), and recurrent neural networks (RNNs)
- Novel Architecture Design: Designed and implemented innovative architectures and algorithms to enhance content generation, driving advancements in quality and performance.
- Retrieval-Augmented Generation (RAG): Implemented RAG architectures to enhance model performance by integrating external knowledge sources. Designed and optimized retrieval systems to improve the relevance and accuracy.
- LangChain Integration: Utilized LangChain to streamline the development of applications leveraging LLMs, facilitating efficient chaining of prompts, tools, and data retrieval for more dynamic interactions.
- Data Management and Preprocessing: Developed robust data pipelines for preprocessing and curating datasets, ensuring high-quality inputs for training and evaluation. Applied techniques like data augmentation to enhance.
Sep 2021 - Present
