We are looking for a highly skilled Retrieval-Augmented Generation (RAG) expert to help us enhance our AI-driven system. The ideal candidate should have deep knowledge of LLMs (Large Language Models), vector databases, embedding models, and NLP techniques to optimize information retrieval and response generation.

Project Scope:

• Implement RAG techniques to improve AI-generated responses.
• Work with vector databases (Pinecone, FAISS, Weaviate, etc.) for efficient retrieval.
• Optimize embedding models for better contextual understanding.
• Fine-tune LLMs (GPT, Llama, or similar) for better accuracy and efficiency.
• Improve document indexing, retrieval speed, and response quality.
• Integrate RAG pipelines into our existing AI framework.
• Conduct testing, evaluation, and optimization for performance improvement.

Skills Required:

• Strong understanding of Retrieval-Augmented Generation (RAG)
• Experience with LLMs (OpenAI, Hugging Face, etc.)
• Proficiency in Python, LangChain, and NLP techniques
• Knowledge of vector databases (FAISS, Pinecone, Weaviate, ChromaDB, etc.)
• Experience in data preprocessing and embedding techniques
• Ability to fine-tune and optimize transformer-based models