Data Engineering for AI-Applications (m/f/d)

at  ZMI GmbH

What to Expect
As a Data Engineer for AI applications at ZMI, you will be part of our innovative Cloud Development Team. You will play a crucial role in developing intelligent and data-driven solutions. In this role, you work at the intersection of data management and machine learning to realize our vision of interdisciplinarily developed new products.

Responsibilities

• Design and implementation of robust data architectures to support AI-driven applications.
• Development and maintenance of data pipelines and ETL processes for data preparation and cleansing, specifically for machine learning and AI.
• Collaboration with AI experts and analysts to define data requirements and optimize data structures.
• Monitoring, maintaining, and continuously improving the data infrastructure to ensure performance and scalability.
• Development of APIs for data integration and querying, utilized by internal analytics tools and external partners.

What You Should Bring

• Profound knowledge in data modeling and managing large volumes of data.
• Experience with database technologies.
• Understanding of the requirements and challenges in building data structures for machine learning and AI.
• Experience working in an agile development environment.

Additional Skills (Advantageous)

• Experience with Big Data technologies.
• Practical experience with cloud platforms like Azure or AWS, particularly with services related to data management and analysis.
• Knowledge in implementing data governance and security measures.
• Curiosity and a strong willingness to learn new technologies and develop innovative solutions.

What We Offer You

• A key role in a pioneering team that develops innovative AI-based cloud applications.
• The opportunity to use the latest technologies and significantly contribute to the future of data-driven product development.
• Flexibility through hybrid work models and extensive opportunities for personal and professional growth in a dynamic environment.

• Design and implementation of robust data architectures to support AI-driven applications.
• Development and maintenance of data pipelines and ETL processes for data preparation and cleansing, specifically for machine learning and AI.
• Collaboration with AI experts and analysts to define data requirements and optimize data structures.
• Monitoring, maintaining, and continuously improving the data infrastructure to ensure performance and scalability.
• Development of APIs for data integration and querying, utilized by internal analytics tools and external partners