ai engineer for AI solutions

GEMA works to ensure that creative professionals receive a fair share of the revenue generated by the use of their musical works, allowing them to focus on their artistic work.

• Identify AI use cases with subject-matter experts, Product Owners, and technical teams, refine business questions, and develop viable approaches, including demos or prototypes where useful
• Work across the AI delivery lifecycle, from data analysis and feature engineering through model development, training, evaluation, and integration into usable services
• Select classical machine learning, deep learning, neural networks, LLM-based solutions, or a practical combination based on each use case
• Bring successful approaches into production-ready or production environments and consider deployment, monitoring, model and data quality, security, traceability, and maintainability from the start
• Contribute to and develop the AI Platform with the team, including components for MLOps, monitoring, labeling, evaluation, and deployment
• Take ownership of projects and work packages and collaborate closely with subject-matter experts, Product Owners, Software Engineers, and Data Engineers

• Have several years of experience developing data- or AI-related applications and be able to advance AI solutions from initial concept to usable application
• Work independently and responsibly, and drive complex topics with a clear focus on results
• Explain complex technical topics clearly, justify solution approaches and model decisions, and present results confidently to business and technical stakeholders
• Be familiar with machine-learning and data-science workflows, including data analysis, feature engineering, model training, evaluation, and understanding model limitations
• Use Python and libraries or frameworks such as Pandas, NumPy, Scikit-Learn, PyTorch, TensorFlow, and XGBoost, or comparable tools; assess classical ML, deep learning, and generative AI approaches for each use case
• Apply software engineering practices including Clean Code, testing, Git, Pull Requests, CI/CD, technical documentation, and maintainable architectures
• Have experience with cloud and MLOps topics
• Communicate confidently and precisely in German, both spoken and written, and use English confidently in a professional environment
• Будет плюсом: Google Cloud, Infrastructure as Code, GitHub Actions, model versioning, pipeline orchestration, monitoring, deployment of ML services

• Part-time option: 30 hours per week
• 30 Days of vacation, flexitime, a home-office arrangement, and workation
• Subsidized company restaurant, barista bar, modern office in a prime location, capital-forming benefits, discounts, and other allowances
• Childcare subsidy, active breaks, pme family service, special leave, and flexible working arrangements