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Evaluate AI-generated antitrust analysis for legal accuracy, build evaluation frameworks and instructions, test edge cases and jurisdictional errors, and write evaluation scripts using AI coding tools. Learn competition law from senior team members and access Concurrences' case law database.

Build and maintain RAG infrastructure for hundreds of thousands of legal documents: chunking pipelines, vector DB integration, embedding strategy, semantic search, retrieval/reranking/LLM orchestration, evaluation tooling, API components, and deployable backend services. Ship multiple features quickly and collaborate with CTO and founder on architecture and domain-driven trade-offs.

Evaluate AI outputs for antitrust accuracy across jurisdictions; build rubrics, annotation guidelines, and tests; design AI instructions; run structured legal experiments; own domains like EU merger control and cartels; write evaluation scripts using AI coding tools.

Build and own end-to-end RAG and inference infrastructure: chunking, embeddings, retrieval optimization, vector DB schema/indexing, LLM integrations, agentic workflows, APIs (Python/FastAPI), and CI/CD/observability for a production legal research engine.