We are a Brazilian law firm seeking an experienced freelancer or team to develop an AI-powered legal research assistant. The core of this system will be a Retrieval-Augmented Generation (RAG) pipeline designed to interact with jurisprudence databases, providing accurate and contextually relevant answers to legal queries. The initial Minimum Viable Product (MVP) will focus specifically on jurisprudence from Santa Catarina, with a clear roadmap for potential expansion to other courts in subsequent phases. We are looking for a robust, production-ready solution.
Key Responsibilities:
- Design and implement a robust RAG pipeline, utilizing modern frameworks such as LangChain, LlamaIndex, or other suitable alternatives.
- Develop and manage the ingestion and indexing process for various legal document formats (pdf, html, txt) into a scalable vector database (e.g., FAISS, Pinecone, Weaviate).
- Integrate a Large Language Model (LLM), such as Claude, OpenAI GPT, or similar, to ensure the generation of highly contextual and precise legal responses.
- Create a user-friendly, web-based interface for the legal assistant, leveraging technologies like React, Streamlit, or equivalent, to facilitate seamless interaction for lawyers.
- Provide comprehensive technical documentation to support ongoing maintenance, future enhancements, and scalability of the system.
Required Skills and Experience:
- Proven expertise in developing and deploying llm-based applications and rag systems.
- Deep understanding of vector databases and the intricacies of document ingestion pipelines.
- Demonstrated ability to integrate complex AI components into production-grade applications.
- Strong full-stack development capabilities, including proficiency in backend (e.g., Python,
Node.js) and frontend (e.g., React) technologies.
- Experience with cloud deployment platforms such as AWS, Google Cloud Platform (GCP), or Microsoft Azure.
- A background in LegalTech or prior experience working with judicial databases is a valuable asset but not strictly required.
Deliverables for the MVP:
- A fully functional mvp of the ai legal assistant, featuring rag-based information retrieval.
- A basic, intuitive web interface for submitting legal queries and displaying results.
- Detailed technical documentation outlining the system architecture, deployment procedures, and guidelines for future scaling and iterations.
Timeline: The target delivery for the MVP is 6–8 weeks. We are open to negotiating the budget based on the proposed scope and the expertise of the candidate. Successful completion of the MVP may lead to long-term collaboration opportunities.
Delivery term: Not specified