• 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.

• 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.

• 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.