Da­ta­ ­Sci­en­tis­t/AI En­gi­nee­r*

• Someone with a strong data/backend foundation (Python, API design) who is not afraid of the frontend.
• Experience or strong interest in React—specifically in how to visualize data (charts, graphs, networks).
• A "Product Engineering" mindset: You care about how the AI output is actually consumed by the user.

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

• You will spend 50% of your working time in our modern and easily accessible office at Vienna Central Station.
• We support your personal development and career planning with an individual and attractive training program. Visit our BAWAG Academy and use our GoodHabitz elearning platform for both your professional and personal growth.
• In addition, we offer a range of interesting and valuable additional benefits.
• For this position, the collective agreement stipulates a minimum annual gross salary of € 51.947,84 on a full-time basis. Depending on your experience and qualifications, we are willing to offer a higher salary.

Our Technology Stack

• AI & Backend Engineering: Python (FastAPI): For building high-performance, asynchronous REST APIs to serve our models and graph data.
• GraphRAG & OpenAI: Leveraging generic Graph approaches and OpenAI models for advanced relationship mapping and knowledge retrieval.
• Graph Databases: (e.g., Neo4j, NetworkX, or similar) for storing and traversing complex knowledge structures.
• Application Interface: Streamlit: For rapid prototyping and internal testing of algorithms. React: (Key Focus) For building the production interface that allows users to visualize, explore, and query our Knowledge Graphs intuitively.
• Data Platform: Snowflake: For scalable analytics and structured data storage.
• Orchestration & DevOps: Airflow: For orchestrating complex data and AI workflows. GitHub & Jenkins: For CI/CD, version control, and automated deployment.

Your contact person: Jutta Durstberger +43 664 80998 22099

* We value people. Would you like to work in a team where good cooperation and diversity are actively practiced? Then you've come to the right place. Equal opportunity is important to us - regardless of age, gender, sexual orientation, physical impairment, religion or origin.