• Build AI-powered workflows, assistants, agents and automation systems. • Apply AI across customer support, CRM, onboarding, claims, renewals, payments, operations and internal tools. • Work with product and engineering teams to turn manual processes into scalable AI-native systems. • Build integrations with LLMs, internal data, APIs, documents, knowledge bases and business systems. • Design evaluation, monitoring and fallback flows so AI outputs are useful, safe and reliable. • Prototype quickly, test with users or operators, then productionize what works. • Improve speed, quality and consistency across workflows using AI where it creates real business value.

• Build AI-powered workflows, assistants, agents and automation systems. • Apply AI across customer support, CRM, onboarding, claims, renewals, payments, operations and internal tools. • Work with product and engineering teams to turn manual processes into scalable AI-native systems. • Build integrations with LLMs, internal data, APIs, documents, knowledge bases and business systems. • Design evaluation, monitoring and fallback flows so AI outputs are useful, safe and reliable. • Prototype quickly, test with users or operators, then productionize what works. • Improve speed, quality and consistency across workflows using AI where it creates real business value.

• Aufbau KI-gestützter Workflows, Assistenten, Agenten und Automatisierungssysteme. • Anwendung von KI in den Bereichen Kundenservice, CRM, Onboarding, Schadensfälle, Verlängerungen, Zahlungen, Betrieb und interne Werkzeuge. • Zusammenarbeit mit Produkt- und Engineering-Teams, um manuelle Prozesse in skalierbare KI-native Systeme umzuwandeln. • Integrationen mit LLMs, internen Daten, APIs, Dokumenten, Wissensdatenbanken und Geschäftssystemen aufbauen. • Entwurf von Evaluierungs-, Überwachungs- und Rückfall-Flows, damit KI-Ausgaben nützlich, sicher und zuverlässig sind. • Schnell Prototypisieren, mit Nutzern oder Betreibern testen und dann das, was funktioniert, in die Produktivumgebung bringen. • Verbesserung von Geschwindigkeit, Qualität und Konsistenz über Workflows hinweg, wo KI echten geschäftlichen Wert schafft.

• Collaborate with experienced data scientists and software engineers to gain insights into building scalable and efficient data pipelines, model training, and deployment systems. • Troubleshoot issues in the entire machine learning infrastructure, from Linux, Docker, and Kubernetes up to the highest levels of our ML stack. Resolve issues, improve system performance, and make our stack the best in the industry. • Assist in the design and development of on-premises MLOps solutions to support the delivery of machine learning models, and a seamless handover between research and productionization of ML artifacts. • Drive and uphold high engineering standards, bringing consistency to codebases encountered and ensuring software is adequately reviewed, tested, and integrated. • Optimize existing models for better performance and throughput. • Incorporate ML model training, validation, and evaluation settings in addition to traditional coding tests like unit and integration testing. • Build and maintain tools for deployment, monitoring, and operations. • Continuously refine and enhance CI/CD workflows to support the evolving needs of the machine learning infrastructure.