Data Scientist (GenAI / RAG)

Digitribe
Belgium
2 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Experienced
Experience required
3 years minimum
Working hours
Regular working hours
Languages
Dutch, English, French
Job source

Tech stack

Artificial Intelligence Microsoft Azure Python (Programming Language) Machine Learning Performance Tuning OpenAI Azure Machine Learning Enterprise Search Retrieval-Augmented Generation Large Language Models Llamaindex Agentic-AI
+5 more
Microsoft Fabric Azure AI Evaluation of Large Language Models Semantic Kernel Databricks

Job description

  • Identify and scope GenAI use cases with business stakeholders, such as knowledge assistants, document analysis and process automation.
  • Design and build RAG solutions, covering document ingestion, chunking, embeddings, vector search and prompt engineering.
  • Develop solutions with Azure OpenAI, Azure AI Search, Azure Machine Learning and related services.
  • Evaluate and improve model performance, including answer quality, hallucination control and relevance metrics.
  • Move prototypes into production with engineering teams, following MLOps/LLMOps practices.
  • Ensure responsible AI principles, data privacy, security and compliance with regulations such as the EU AI Act and GDPR.
  • Share knowledge and help raise AI maturity within client teams.

Requirements

The ideal candidate will have:

  • At least 3 years of experience in data science or machine learning, with recent hands-on work on GenAI/LLM projects.
  • Practical experience building RAG pipelines, using frameworks such as LangChain, LlamaIndex or Semantic Kernel.
  • Strong Python skills and a solid grounding in NLP and machine learning fundamentals.
  • Experience with Azure AI services such as Azure OpenAI and Azure AI Search.
  • Ability to explain AI concepts, limits and risks to non-technical audiences.
  • Fluent English, French and/or Dutch.

Nice to have:

  • An Azure AI certification (AI-102 or DP-100).
  • Experience with vector databases, agentic workflows, fine-tuning or LLM evaluation frameworks.
  • Experience with Databricks or Microsoft Fabric.
  • Experience in banking, insurance or other regulated sectors.

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