• Entwicklung und Evaluation von KI-, Machine-Learning- und Deep-Learning-Modellen zur Analyse multisensorischer Daten. • Verarbeitung, Analyse und Qualitätssicherung großer Sensor- und Umweltdatensätze (z. B. Audio-, Bild-, Spektral- und IoT-Daten). • Konzeption und Implementierung von Datenpipelines für die Erfassung, Vorverarbeitung und Auswertung von Sensordaten. • Entwicklung von Prototypen für Edge-AI-, IoT- und Cloud-Anwendungen im Bereich intelligenter Sensorsysteme. • Mitarbeit an Forschungs- und Entwicklungsprojekten sowie Dokumentation, Visualisierung und wissenschaftliche Auswertung von Ergebnissen.

• Build ML models for production use. • Perform in-depth exploratory data analysis to scope ML opportunities, identify potential issues in the data, and lay the groundwork for model design, training & evaluation strategies. • Suggest and implement new features to improve model performance and business logic. • Write and review production-quality PRs daily. • Translate client business problems into ML problems. • Build scalable machine learning models for claim time series forecasting and NLP text understanding. • Continue to grow and learn in the data & ML space with a focus on business problems that require making sense of large-scale structured and unstructured datasets.

• Design and development of machine learning solutions, ensuring accuracy, performance, security, and scalability. • Implement and maintain end-to-end AI/ML pipelines - from data ingestion and feature engineering through to model development, validation, and deployment with guidance from senior engineers on complex architectural decisions • Instrument AI/ML services with appropriate metrics, logging, and telemetry to monitor model performance and operational health against defined SLOs • Participate in on-call rotations, executing progressive rollouts and applying standard mitigation strategies to keep production inference services healthy • Collaborate across planning, design, and code review phases contributing to product and technical discussions, while helping raise overall code quality through thoughtful review feedback

• Design and development of machine learning solutions, ensuring accuracy, performance, security, and scalability. • Implement and maintain end-to-end AI/ML pipelines - from data ingestion and feature engineering through to model development, validation, and deployment with guidance from senior engineers on complex architectural decisions • Instrument AI/ML services with appropriate metrics, logging, and telemetry to monitor model performance and operational health against defined SLOs • Participate in on-call rotations, executing progressive rollouts and applying standard mitigation strategies to keep production inference services healthy • Collaborate across planning, design, and code review phases contributing to product and technical discussions, while helping raise overall code quality through thoughtful review feedback