Data Scientist – RAG & Document Intelligence

Craftware is a technology company of over 500 experts, empowering large organizations to solve complex business challenges with modern IT solutions. They are building a platform that lets business users retrieve, synthesize, and act on information locked inside complex enterprise documents using plain language.

• Design, experiment with, and continuously optimize RAG pipelines – chunking strategies, embedding models, hybrid search, re-ranking, and context assembly
• Benchmark and evaluate LLMs, embedding models, and document parsing solutions across accuracy, latency, reliability, and cost
• Build evaluation datasets and define RAG-specific quality metrics using frameworks like RAGAS, DeepEval, and Langfuse
• Design and iterate on multi-agent system architectures (LangGraph, LangChain, Pydantic AI)
• Handle diverse, noisy document formats – PDFs, Word docs, presentations, scanned files, tables, charts, and mixed-format corpora
• Track token consumption, latency profiles, and retrieval quality – making principled trade-off decisions between capability and operational cost
• Shape a system that will serve business users across commercial, marketing, R&D, and product supply globally

• Strong Python skills with hands-on experience in ML and generative AI workflows, including production-grade code
• Solid NLP background – text representation, semantic search, embeddings, language model behaviour
• Deep hands-on experience designing and optimizing RAG pipelines – chunking, hybrid search, re-ranking (LlamaIndex, LangChain, LightRAG)
• Experience with document parsing across diverse and noisy formats, including tables, charts, and figures
• Experience with LLM evaluation frameworks and RAG-specific quality metrics (RAGAS, DeepEval, Langfuse)
• Familiarity with multi-agent AI frameworks – LangGraph, LangChain, or Pydantic AI
• Experience with vector databases (pgvector, Pinecone, Qdrant, Weaviate)
• Experience with Azure cloud services (Apps, Containers, Storage, AI Search, AI Foundry) and/or AWS equivalents
• Experience with Databricks (Delta Lake, Unity Catalog, MLFlow)
• Structured, experiment-driven approach to problem solving
• Fluent English – written and spoken

• B2B contract
• Daily support from team leaders
• Dedicated certification budget
• Assistance in defining and support in your development path
• Benefits package
• Integration trips/events