Your responsibilities: Build and maintain end-to-end MLOps / LLMOps pipelines (CI/CD, monitoring, retraining). Deploy, scale, and operate high-availability APIs and model-serving services for anomaly detection and predictive data quality scoring. Integrate advanced AI tools (e.g., LangChain, Portkey, Galileo, HuggingFace Transformers) into data quality agents and automated error-classification workflows. Collaborate with Data Engineering and IT Ops teams to manage AWS-based infrastructure for scalable model serving and monitoring. Implement comprehensive performance, drift, and data-quality monitoring with automated retraining loops. Ensure all AI systems comply with internal security guidelines and GxP requirements. We are looking for you, if you have: Proven experience as an AI Engineer, ML Engineer, or MLOps Engineer in production environments. Strong expertise with MLOps platforms (AWS SageMaker, Kubeflow, MLflow). Advanced Python skills and ability to deliver robust, tested production code. Hands-on experience with Docker and Kubernetes. Experience deploying Transformer or LLM-based models (HuggingFace, LangChain). Solid knowledge of AWS services (Lambda, EC2, SageMaker). Strong understanding of system design, distributed computing, and data streaming.  Nice to Have Experience in regulated GxP environments. Familiarity with Pharmacovigilance (PV), clinical trial data, or Master Data Management. We offer: Participation in interesting and challenging projects. Flexible working hours. A great, non-corporate atmosphere. Opportunities for development and promotion. Attractive package of benefits. Work model: remote or hybrid (2 days per week from the office) We reserve the right to contact the selected candidates.