AI/ML Data Engineer

About the role

The AI/ML Data Engineer builds and maintains production pipelines that turn unstructured text and documents into reliable, model-ready data for the company's Generative AI and NLP applications. The focus is on data products built from licensed scientific literature, patents and clinical trials, supporting use cases from early-stage research to post-market surveillance. The engineer works alongside data scientists and subject matter experts and is mentored by senior engineers.

Key responsibilities

• Build and maintain production data pipelines, transformations and automated workflows that turn scientific text into data products
• Clarify requirements, identify gaps and assumptions and raise questions before and during implementation
• Adopt and contribute to data engineering practices, propose schemas and take part in code reviews
• Build automated schema, completeness and constraint checks and create test datasets for integration and regression testing
• Monitor pipelines, troubleshoot failures, apply upgrades and keep data products reliable and cost-efficient
• Support deployment of ML pipelines into production and use coding agents in daily development

Required profile

• 3+ years of professional experience in data engineering or a closely related area
• Bachelor's degree in Computer Science, Data Engineering or a related technical field, or equivalent practical experience

Required skills

• 2+ years building data pipelines with Python and SQL
• Data engineering, data standards, data storage, Git, GitHub, GitHub Actions, machine learning, testing

What we offer

• Hybrid working model and flexible role pattern
• Competitive salary and incentive pay, pension and health insurance contributions
• 5 weeks annual leave, 5 sick days and 15 days of certified sick leave paid above statutory requirements
• Cafeteria with tax-free benefits, multisport card, Vodafone, Raiffeisen Bank, Foodora and discount programs
• Up-to-date laptop and iPhone, parking, showers, refreshments, massage chairs, library and music corner
• Learning platforms, conference attendance and internal communities of practice