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# Data Scientist/AI Engineer

- **Company:** BAWAG P.S.K.
- **Location:** Wien, Austria
- **Salary:** €51,948.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Big Data, Continuous Integration, Data Infrastructure, Data Visualization, Database Queries, DevOps, Github, Graph Database, Python (Programming Language), Rapid Prototyping Process, Software Engineering, Data Storage Technologies, ReactJS, Delivery Pipeline, Snowflake, Backend, Fastapi, Deployment Automation, Front End Software Development, Api Design, Streamlit Framework, Software Version Control, Jenkins
- **Published:** August 25, 2026
- **Apply:** https://career55.sapsf.eu/careers?company=BawaggroupP

## About the Role

* Someone with a strong data/backend foundation in Python and API design who is not afraid of the frontend.
 * Experience or strong interest in React, specifically in visualizing data such as charts, graphs and networks.
 * A product engineering mindset and care for how AI output is consumed by users.

## Description

* AI & Backend Engineering: Python (FastAPI), GraphRAG & OpenAI, and graph databases such as Neo4j, NetworkX or similar.
 * Application Interface: Streamlit for rapid prototyping and React for production interfaces that allow users to visualize, explore and query Knowledge Graphs intuitively.
 * Data Platform: Snowflake for scalable analytics and structured data storage.
 * Orchestration & DevOps: Airflow, GitHub and Jenkins for workflows, version control, CI/CD and automated deployment.

What You'll Do

 * Engineer AI Backends: Design and deploy scalable FastAPI endpoints that handle complex queries and serve data from Knowledge Graphs to the frontend.
 * Build Interactive Graph Interfaces: Take proven concepts from Streamlit and re-engineer them into responsive React applications for business users.
 * Optimize GraphRAG Pipelines: Refine RAG strategies to ensure AI retrieves the most relevant contexts from graph structures.
 * Productionize Prototypes: Bridge data science and software engineering by rewriting experimental Python code into production-grade, maintainable software.
 * CI/CD & Deployment: Use Jenkins and GitHub to maintain robust deployment pipelines for backend APIs and frontend applications.


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