Graphrag Developer Challenge

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

Descrizione del lavoro

A leading tech company in Italy seeks a Senior RAG Systems Developer for a paid technical evaluation. The role involves building a high-accuracy prototype for legal document reasoning, implementing backend functions in Python, and conducting a live demo. Expert knowledge in graph-based retrieval and a focus on accuracy are critical for success in this position.

Competenze

• Expertise in graph-based retrieval systems.
• Proficiency in Python for backend development.
• Experience with the construction of knowledge graphs.

Mansioni

• Build a high-accuracy prototype for legal document reasoning.
• Implement functions to ingest documents and answer queries.
• Conduct a live demo showcasing system architecture and performance.

Conoscenze

Graph-based retrieval

Python 3.12

Knowledge graph construction

Multi-hop reasoning

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)