Key responsibilitiesDefine the technical vision and system architecture for AI-powered platforms and products in the podConvert evolving product and research requirements into scalable, reliable ML systemsPartner with the Senior Principal Product Manager to align technical decisions with product strategyPartner with the Senior Principal Applied Scientist and Principal Scientists to take models from experimentation into production grade systemsOwn architectural decisions across model training, inference, data pipelines, and system integrationIdentify critical risks early in performance, scalability, cost, and reliability, and drive solutionsLead technical design reviews and influence architecture across multiple engineering teamsEstablish best practices for ML system design, observability, testing, and long-term maintainabilityMentor senior engineers and serve as a technical role model in the organizationBasic qualifications8+ years of professional software engineering experience, including significant work on AI or ML-powered systemsDemonstrated experience designing, building, and scaling complex distributed systemsStrong understanding of the end-to-end machine learning lifecycle, including deployment and monitoring in productionDemonstrated ability to lead architectural efforts and influence technical direction beyond your immediate teamProficiency in at least one backend systems programming language: Python, Go, Java, or similarStrong system-level reasoning across performance, scalability, fault tolerance, and cost tradeoffsPreferred qualificationsExperience bridging machine learning research and production engineeringFamiliarity with generative AI systems, large language models, or multimodal pipelinesExperience building systems that interact with the physical world or real time environmentsBackground in ML infrastructure, model serving, inference optimization, or training infrastructure at scaleExperience mentoring senior engineers or acting as a technical lead across teamsPrior collaboration with globally distributed engineering or research organizations Job ID 509365 Posted since 08-Jun-2026 Organization Data & Artificial Intelligence Field of work Research & Development Company Siemens Ltd., Key responsibilitiesDefine the technical vision and system architecture for AI-powered platforms and products in the podConvert evolving product and research requirements into scalable, reliable ML systemsPartner with the Senior Principal Product Manager to align technical decisions with product strategyPartner with the Senior Principal Applied Scientist and Principal Scientists to take models from experimentation into production grade systemsOwn architectural decisions across model training, inference, data pipelines, and system integrationIdentify critical risks early in performance, scalability, cost, and reliability, and drive solutionsLead technical design reviews and influence architecture across multiple engineering teamsEstablish best practices for ML system design, observability, testing, and long-term maintainabilityMentor senior engineers and serve as a technical role model in the organizationBasic qualifications8+ years of professional software engineering experience, including significant work on AI or ML-powered systemsDemonstrated experience designing, building, and scaling complex distributed systemsStrong understanding of the end-to-end machine learning lifecycle, including deployment and monitoring in productionDemonstrated ability to lead architectural efforts and influence technical direction beyond your immediate teamProficiency in at least one backend systems programming language: Python, Go, Java, or similarStrong system-level reasoning across performance, scalability, fault tolerance, and cost tradeoffsPreferred qualificationsExperience bridging machine learning research and production engineeringFamiliarity with generative AI systems, large language models, or multimodal pipelinesExperience building systems that interact with the physical world or real time environmentsBackground in ML infrastructure, model serving, inference optimization, or training infrastructure at scaleExperience mentoring senior engineers or acting as a technical lead across teamsPrior collaboration with globally distributed engineering or research organizations.