I will build a production ready hybrid rag pipeline and ai chatbot

Your AI proof-of-concept works in basic demos, but in production, the RAG system hallucinates on real documents.

I am a Generative AI Engineer who builds the production-grade engineering layer that makes AI systems work safely in corporate environments. I specialize in connecting secure vector databases with LLMs using Python, LangChain, and AWS Bedrock/Azure OpenAI.

What I Engineer for You:

• Advanced Hybrid RAG Pipelines: Combining dense embeddings, sparse models (BM25), and cross-encoder re-ranking to improve retrieval precision by 35% against baseline vector search.
• Optimized Data Summarization: Securely summarizing enterprise data by generating SQL queries via LLMs, rather than feeding massive, costly datasets directly into context windows.
• Enterprise Integration: Architecting serverless ETL pipelines (e.g., AWS Step Functions, Apache Iceberg) to move and transform data securely without persistent compute costs.
• Direct Source Attribution: Mandatory citations and human-in-the-loop evaluation frameworks for every claim generated to reduce hallucinations.

Let's design a semantic-cached, optimized infrastructure. Please message me before ordering!