Data Engineer
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The role is about owning the data pipelines behind a legal knowledge base and the processing of users' uploaded documents, from reading scanned files to tagging and anonymizing them. A strong candidate has about four years of data engineering, solid Python, and experience running ETL pipelines in production with monitoring in place. Fluent Italian and English and a willingness to be in Milan are also part of the picture.
How it fits the AI era
AI tools can speed up the routine parts, such as drafting pipeline code, cleaning text and tagging documents, while language models now do some of the document reading itself. What stays human is judging data quality, deciding how sensitive client files are handled, and designing systems that stay reliable at scale. In legal work, one wrong link between sources or one privacy slip matters a lot, so careful ownership counts for more than speed.
How it builds AI experience
The work sits right behind an AI legal assistant, so there is direct exposure to how search, vector databases and document understanding feed AI answers. Working with AI Engineering and Legal Engineering gives a close view of what data makes a legal AI trustworthy. Someone new to this could build a small project that extracts text from scans, tags it and stores it in a vector database.
Worth showing
- Production ETL pipelines in Python
- Monitoring and data quality checks
- Vector and SQL database experience
- OCR or LLM document processing
- Handling sensitive personal data
This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.
About Lexroom.ai
Lexroom is an AI platform for legal professionals that handles legal research, document analysis and drafting of legal documents, per the company.
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