AI Software Developer for LLM-Based RAG System -- 2
Budget: $30 – $250 AUD
Title: Expert AI Software Developer for LLM-Based RAG System & Azure Bug Fixes
We are looking for an experienced AI Software Developer to lead the implementation and optimization of a Retrieval-Augmented Generation (RAG) system. The primary responsibilities include:
Data Ingestion & Vectorization: Processing PDFs and text documents into a local vector database, tagging/classifying them with namespaces for contextual isolation.
Smart Retrieval & Summarization: Implementing LLM-based retrieval and summarization using models like LLaMA and ChatGPT.
Custom Prompt Management: Enabling prompt chaining and reuse through a structured prompt library.
UI Customization: Integrating and customizing the Open-WebUI interface to ensure an intuitive user experience.
Bug Fixes: Addressing NLP-related issues, fixing Azure deployment problems, and correcting query system errors.
An immediate start is required. Candidates must demonstrate a working RAG implementation in their local environment during the selection process. This is a hands-on technical role—only freelancers with proven experience in deploying RAG stacks, utilizing LangChain/LlamaIndex, managing vector databases (e.g., FAISS, Chroma), and customizing open-source interfaces will be considered.
We are looking for an experienced AI Software Developer to lead the implementation and optimization of a Retrieval-Augmented Generation (RAG) system. The primary responsibilities include:
Data Ingestion & Vectorization: Processing PDFs and text documents into a local vector database, tagging/classifying them with namespaces for contextual isolation.
Smart Retrieval & Summarization: Implementing LLM-based retrieval and summarization using models like LLaMA and ChatGPT.
Custom Prompt Management: Enabling prompt chaining and reuse through a structured prompt library.
UI Customization: Integrating and customizing the Open-WebUI interface to ensure an intuitive user experience.
Bug Fixes: Addressing NLP-related issues, fixing Azure deployment problems, and correcting query system errors.
An immediate start is required. Candidates must demonstrate a working RAG implementation in their local environment during the selection process. This is a hands-on technical role—only freelancers with proven experience in deploying RAG stacks, utilizing LangChain/LlamaIndex, managing vector databases (e.g., FAISS, Chroma), and customizing open-source interfaces will be considered.
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