Multi Agent RAG Pipeline Enhancement & Multi-Document Embedding Integration
Budget: $30 – $250 USD
We are looking for an experienced Generative AI / LLM Engineer to enhance our existing multi-agent RAG pipeline built with agentic orchestration and GPT-5 models. The main goal is to improve multi-document embedding, retrieval accuracy, and agent workflow coordination.
Responsibilities:
Enhance and optimize the multi-agent RAG architecture
Implement multi-document embedding and indexing
Improve semantic search and context retrieval
Optimize prompts and workflows for GPT-5
Integrate and tune vector databases
Requirements:
Strong experience with RAG systems
Experience with LLMs and agent-based workflows
Proficiency in Python
Experience with vector databases (Pinecone, FAISS, Chroma, etc.)
Nice to Have:
Experience with LangChain / LlamaIndex
Experience building multi-document AI systems
Please share examples of RAG or LLM projects you have worked on.
Responsibilities:
Enhance and optimize the multi-agent RAG architecture
Implement multi-document embedding and indexing
Improve semantic search and context retrieval
Optimize prompts and workflows for GPT-5
Integrate and tune vector databases
Requirements:
Strong experience with RAG systems
Experience with LLMs and agent-based workflows
Proficiency in Python
Experience with vector databases (Pinecone, FAISS, Chroma, etc.)
Nice to Have:
Experience with LangChain / LlamaIndex
Experience building multi-document AI systems
Please share examples of RAG or LLM projects you have worked on.