AI-Integration Engineer (w/m/d)

The AI ​​Integration Engineer plays a central role in the technical and organizational transition of AI solutions from MVP and delivery phases to stable, productive operation. In this role, they define infrastructure-related standards for deployment, integration, and operation; bridge the gap between ML engineers/data scientists, business IT/service clusters, AI governance, and platform and infrastructure functions; and ensure that go-live, monitoring, and lifecycle management are considered from the outset and continuously managed after go-live.

• Strategic and operational design of the requirements and standards for the go-live, takeover and scaling of AI solutions for all AI units at Amprion – taking into account the applicable EU AI Act requirements, starting as early as the PoC/MVP phase.
• Definition of procedures and technical frameworks for the production deployment of AI solutions within and outside of Azure (e.g., architectural patterns, security and compliance requirements, operating models)
• Conceptualization and further development of deployment standards, CI/CD pipelines, reference architectures and technical frameworks for MLOps (including Azure ML) in close coordination with platform, infrastructure and security teams.
• Responsibility for model lifecycle management: release processes, drift detection, retraining and versioning of models, as well as performance and scaling optimization (e.g., quantization, pruning, batch/streaming processing, use of GPUs)
• Overall management, structuring and moderation of the handover points between AI teams, business IT / service clusters and specialist departments; clarification and documentation of clear responsibilities for data connection, monitoring, support and licensing.
• Testing and supporting the productive integration and operational capability of models and AI solutions (including scaling, robustness, observability, incident/change handling) as well as shared responsibility for the operation and monitoring of existing productive AI services.

• Completed university degree in computer science, business informatics, engineering or a comparable field of study, or an equivalent qualification acquired through practical experience in the field of software, data or cloud/AI engineering.
• Several years of professional experience (e.g., 3–5 years) in the technical integration, operation, or architecture of data-driven or (AI) solutions in enterprise environments; experience in complex enterprise landscapes is an advantage.
• Excellent knowledge of deployment standards and cloud and platform technologies, preferably Microsoft Azure (e.g., Azure ML, Kubernetes, container orchestration, API management), as well as practical experience in building and operating CI/CD pipelines and MLOps environments.
• Experience in collaborating with data scientists, ML engineers, software developers, IT operations, and business departments; ability to communicate technical requirements and operational necessities in a way that is appropriate for the target audience.
• Knowledge of topics such as monitoring/observability, logging, performance optimization, security, and compliance in the context of productive AI workloads; willingness to further deepen this knowledge in collaboration with specialist and security teams.
• Independent and structured work style with a focus on quality and feasibility, combined with pragmatism in finding solutions.