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 - from sales systems and automation to data platforms and AI. We operate where technology must be reliable, secure, and scalable. We deliver end-to-end projects: from analysis and architecture through implementation to development and maintenance. We are a trusted partner of industry leaders such as Salesforce, Veeva, UiPath, and Databricks.

Model: remote

Employment type: full-time

Project

You will be at the heart of one of the most exciting AI initiatives at an international Consumer Health company: building a platform that lets business users retrieve, synthesize, and act on information locked inside complex enterprise documents using plain language.

This is a greenfield, research-meets-engineering role where you will design, experiment with, and implement a context-aware, multi-agent AI system – and the decisions you make will shape how the entire organization interacts with its knowledge assets.

Responsibilities:

• 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

Requirements:

• 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

Nice-to-Have

• Databricks GenAI products: Vector Search, Agent Framework, Knowledge Assistance, Genie, Agent Bricks

• API development and integration (FastAPI)

• Knowledge of Model Context Protocol (MCP) for tool integration

• Understanding of knowledge management, taxonomy design, and metadata enrichment for enterprise document repositories

Employment conditions:

• B2B contract,

• Daily support from team leaders,

• Dedicated certification budget,

• Assistance in defining and support in your development path,

• Benefits package,

• Integration trips/events.