Senior AI Data Engineer for RAG & AI Data Pipelines

Billennium; Wrocław; On-site; PLN 180,000 - 240,000; Full time

Benefits offered by this job

Udemy for Business

Private medical care

Multisport card

Language lessons

Welcome pack

Job summary

Billennium seeks a Senior AI Data Engineer / Data Scientist to transform complex, unstructured enterprise data into AI-ready knowledge assets. You will own the data lifecycle for AI use cases—from discovery, cleaning, normalization and enrichment through ingestion, retrieval optimization and refresh cycles.

You will work hands-on with SharePoint repositories, documents, PDFs, Office files, wikis, databases and data lakes, preparing them for use in RAG systems, AI agents and enterprise copilots.

Qualifications

• 5+ years of professional experience in Data Engineering, Data Science or Analytics Engineering.
• Proven experience owning production-grade data pipelines.
• Strong Python skills for data processing, parsing and pipeline development.
• Hands-on with unstructured enterprise data (documents, PDFs, Office files, wikis).
• Experience preparing data for retrieval, NLP or RAG use cases including embeddings.

Responsibilities

• Lead enterprise data discovery and cleanup for AI use cases.
• Build and maintain ingestion pipelines for SharePoint and enterprise repositories.
• Prepare data for retrieval and RAG-based AI applications, including chunking and metadata enrichment.
• Define data quality gates: freshness, completeness, relevance, duplication, metadata coverage.
• Collaborate with AI Engineers and business stakeholders to define data requirements and structure.

Skills

Data pipelines

Python

Unstructured data

AI/RAG architectures

Data quality engineering

Retrieval concepts

Collaboration with AI teams

Tools

Postgres + pgvector

Langfuse

Microsoft Presidio

Job description

Billennium seeks a Senior AI Data Engineer / Data Scientist to transform complex, unstructured enterprise data into AI-ready knowledge assets. You will own the data lifecycle for AI use cases—from discovery, cleaning, normalization and enrichment through ingestion, retrieval optimization and refresh cycles.

You will work hands-on with SharePoint repositories, documents, PDFs, Office files, wikis, databases and data lakes, preparing them for use in RAG systems, AI agents and enterprise copilots.