EU remote
Senior AI Integration Engineer Go-to-Market (f/m/div.)
About this role
Your contribution to something big: Shape AI architecture: Define solution architectures, agentic interaction patterns, and engineering standards for Generative AI solutions, including tool calling, APIs, Model Context Protocol (MCP), and robust fallback mechanisms. Enable scalable AI integration: Establish reusable patterns for integrating AI capabilities with enterprise platforms, applications, data, and user interfaces.
Drive standardization and governance: Develop pragmatic standards and production-readiness criteria that help AI product teams move efficiently from prototypes to reliable, scalable solutions. Connect technology and security: Collaborate with IT and security functions to align AI architectures with approved enterprise platforms, identity and access management (IAM), cybersecurity, and compliance requirements. Be a technical sparring partner: Support developers, architects, and use case owners as a technical authority and trusted partner throughout the AI solution lifecycle.
Evaluate what comes next: Assess emerging AI models, agent frameworks, and external technologies with regard to scalability, security, performance, and cost efficiency. Prototype new approaches: Build and validate prototypes to explore new architectural patterns and accelerate the adoption of promising AI technologies. What distinguishes you: Education: Degree in Computer Science, Artificial Intelligence, Software Engineering, Data Science, or a comparable field.
Experience: Several years of experience in software, solution, or AI architecture. Proven experience designing and bringing production-grade AI or Generative AI solutions into operation. Experience working across multiple teams, products, or international environments is an advantage. Know-how: Strong hands-on experience with AI and Generative AI platforms, architectures, or applications. Solid understanding of enterprise software architecture, APIs, data architectures, and cybersecurity.
Practical knowledge of agentic AI, tool calling, AI integration patterns, or Model Context Protocol (MCP). Ability to translate privacy, security, and compliance requirements into pragmatic engineering standards. Experience evaluating emerging AI technologies and balancing technical potential with scalability and cost efficiency. Working Style and Methods: You enjoy turning complex requirements into simple, scalable, and reusable solutions.