Senior Solution Architect - Analytics and AI applications

The Data Assets, Analytics & AI team at Bayer Consumer Health focuses on digital transformation and innovation by creating analytical solutions that enable data-driven decision making and performance optimization for Bayer Consumer Health organization.

• Bachelor or Master degree in Computer Science, Engineering, or similar.
• 5+ years experience in cloud native application architecture, especially with Azure, microservices, containerization/Docker, serverless computing, event-based and service mesh architectures.
• Proficient knowledge of Microsoft Azure services including Azure Container Apps, Azure Functions, Azure SQL, and Storage accounts.
• Good understanding of frameworks/components for application development in Python (e.g. Django, Flask, FastAPI) and JavaScript UI Frameworks (React.js, Next.js).
• Experience with data analytics and AI services such as Azure Databricks, MLFlow, vector databases, RAG, AI foundry, and frameworks for AI agents is a plus.
• Expert in GitHub ecosystem including version control, GitHub Actions for CI/CD, and ticket/pull request workflows.
• Understanding of ML/Ops practices is a plus.
• Experience applying Agile methodologies (Scrum, Kanban).
• Strong problem solving, analytical, interpersonal, communication, and presentation skills.
• Fluent in English, written and spoken, with intercultural awareness and willingness to travel.

• Lead the technical implementation of cloud native data analytics and AI applications such as advanced data visualization tools, forecasting, data maintenance applications, and AI chatbots.
• Collaborate closely with data scientists, AI-engineers, full stack developers, data architects, dev/ops engineers, and product managers to define project scopes and deliverables.
• Translate functional and non-functional requirements into technical concepts considering robustness, scalability, reusability, cost efficiency, performance, security, and data privacy.
• Recommend and lead creation of reusable components like UI components, authentication handlers, and database connection handling.
• Partner with data scientists and AI-engineers to assess frameworks and tools like vector databases and LLM integration frameworks.
• Stay updated on latest trends and advancements in application engineering.
• Collaborate with the cloud platform team to enhance the global cloud-based data, analytics and AI platform.
• Support prototypes and proof-of-concept applications with architecture concepts and validate design concepts and technologies.
• Produce comprehensive documentation including architectural diagrams, design specifications, and user manuals.
• Guide application developers as architects, provide cost and time estimations, challenge vendor costs, assure quality delivery, and ensure proper handover to support.