Graphrag Developer Challenge

Bullaki; Ancona; Remoto; EUR 30.000 - 50.000; Part-time

Descrizione del lavoro

A technical prototyping company is seeking a Senior RAG Systems Developer for a paid challenge focused on legal document processing. You will build a high-accuracy prototype that utilizes graph-based retrieval techniques. Successful candidates will potentially transition to a long-term position. The job involves implementing functions in Python and demonstrating your work in a live session, aiming for high accuracy and performance.

Competenze

• Expert in graph-based retrieval (GraphRAG) for legal document processing.
• Experience with Python 3.12 and building high-accuracy prototypes.
• Ability to test thoroughly for correctness and performance.

Mansioni

• Build a high-accuracy prototype for legal document reasoning.
• Implement functions to ingest documents and query with answers.
• Present architecture and demonstrate functionality in a live demo.

Conoscenze

graph-based retrieval

Python 3.12

knowledge graph construction

parallel execution

performance testing

Descrizione del lavoro

Role Details

GraphRAG Developer Challenge – Legal Document Processing (Prototype) Role : Senior RAG Systems Developer (Contract / Freelance) Compensation : $600 — paid only if you pass ()

95% benchmark) Timeline : 3–5 days from materials receipt to live demo Purpose : Technical evaluation for potential long-term hire Frontend / UI : None (backend prototype only) Contact : Objective We’re seeking an expert in

graph-based retrieval (GraphRAG)

to build a high-accuracy prototype for legal document reasoning. This is a paid technical test that may lead to a long-term position. The goal is a true GraphRAG system featuring explicit knowledge-graph construction and traversal, multi-hop reasoning, agentic orchestration, and strong focus on retrieval accuracy and explainability. Materials Provided / docs / → Pre-processed Markdown legal documents with metadata / sample_questions.json → Example question format / sample_answers_rag.json → Example answer format Download materials : (Benchmark uses unseen questions.)

Deliverables

Implement two functions in Python 3.12 (Poetry project) :

• def ingest(document_paths: List(str)) -> None : """Ingest Markdown docs and build the knowledge graph."""
• def query(questions: List(str)) -> List(str) : """Return answers with Vancouver-style citations grounded in retrieved sources."""

Requirements

• No UI, no API keys provided. Any stack may be used.
• query(...) must support parallel execution (~400 questions in ≤60 min) and show a progress indicator.
• Test thoroughly for correctness and performance before the demo.

Live Demo

In a 60-minute live session you will : Receive ~400 unseen questions. Run query(...) to produce /answers.json. Explain your architecture : how the graph is built, traversed, and used to generate grounded answers.

developer(s) who wrote the code may present.

Evaluation

Evaluation Passing requires an overall score above 95%, measured by (LLM as a judge) :

• Faithfulness (grounded, no hallucinations)
• Relevance (retrieval matches intent)
• Completeness (covers key legal points)
• Clarity (structured, legally coherent writing)