Connect to client systems — databases, APIs, CRMs, and other SaaS tools — and get data reliably out of and into them.
Extract, clean, transform, and move data so it is structured and usable by AI features, including retrieval and agentic workflows.
Build and maintain data pipelines that feed the Applied AI Engineer’s RAG and agent systems with current, correct data.
Use AI coding tools to accelerate integration work, while applying real engineering judgment to the bespoke, messy parts those tools handle poorly.
Handle the practical realities of client data: undocumented schemas, inconsistent formats, partial records, and access constraints.
Apply data-handling, security, and privacy best practices throughout, especially with client data.
Collaborate with the Builder and the Applied AI Engineer to ensure data flows cleanly end to end, and document integrations and pipelines in Notion so they are repeatable and maintainable.

Currently enrolled or recently graduated from a Master’s program in Computer Science, Software Engineering, Data Engineering, Information Systems, or a related field.
Solid back-end and data fundamentals: Python and/or Node.js, plus comfort with SQL and relational databases.
Demonstrated experience using AI coding tools (Cursor, Copilot, Claude Code, Windsurf, or similar) as a core part of how you build.
Hands-on experience moving data between systems — working with APIs, building simple pipelines, or cleaning and transforming real datasets.
Ability to read, evaluate, and improve AI-generated code rather than accepting it blindly, particularly for integration logic.
Understanding of databases, APIs, and asynchronous request handling.
Comfort working with incomplete, messy, or undocumented data and making sensible assumptions.
Awareness of data security and privacy best practices.
Excellent verbal and written communication skills within a cross-functional team environment.

AI Integration and Data Engineer – Master-Level Internship

Key skills