Principal AI Engineer - Conversational Banking

About us N26 is Europe's leading digital bank, fully licensed in Germany and active across more than 20 markets. The mission is not simply to build another fintech product, but to build Europe's most trusted digital primary bank account — the account where people's salaries land, their bills are paid, and their financial lives are managed. That ambition shapes how we build. As we enter a new phase of maturity, our focus is on scalability, robustness, and the kind of operational excellence that earns everyday trust — not just signups. New senior leaders have joined to drive this chapter, and we're sharpening how we ship and sustain products at scale. We're also expanding into the moments that make a bank indispensable, from mobile connectivity to financial tools designed for families. These aren't adjacent experiments; they're part of a deliberate strategy to be embedded in daily life. About the role We are making a strategic investment in Artificial Intelligence and Generative AI to redefine how customers interact with N26. We are looking for a Principal AI Engineer to architect the next generation of intelligent, multi-channel conversational ecosystems and automated backend intelligence. In this role, you will support our vision for Conversational Banking powered by AI, and building seamless automation for Operations. Serving as the ultimate technical authority and productionizing partner for our Data Science, Engineering, and Product teams, your mission is delivery optimized for velocity, reliability, and modular design. You will oversee the transition of AI/ML models, LLM prototypes, and autonomous agents directly into high-traffic product infrastructure. Your impact Drive Architectural Strategy & Scalability: Partner across Data Science, Platform Engineering, and Product teams to design the technical architecture, data pipelines, and orchestration systems required for high-traffic AI services. Ensure the ecosystem supports highly available, low-latency APIs. Productionize Complex AI Prototypes: Act as the primary technical bridge transforming data science prototypes into customer-facing features. Refactor, deploy, and maintain Python and Kotlin services, including LLM-powered applications and intelligent agents, within production infrastructure. Design Advanced AI & Agentic Frameworks: Build and optimize application logic surrounding foundational models (leveraging AWS Bedrock and Anthropic). Directly implement advanced techniques, including Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP) servers, and Agent-to-Agent (A2A) orchestration. Establish Observability & Evaluation Standards: Implement logging, tracing, and metrics frameworks tailored for LLM outputs. Design and execute real-time evaluation frameworks to measure output quality, manage prompt updates safely, and run A/B tests across different models. Cross-Functional Mentorship: Leverage deep system expertise to identify and recommend new AI use cases based on production feasibility and data availability. Cultivate a collaborative environment centered on mutual learning, knowledge transfer, and code quality across engineering disciplines. What you bring Proven Production Experience: Extensive track record of building, deploying, and maintaining machine learning models or LLM-based applications in high-volume production environments. Advanced Backend Engineering: Mastery of developing scalable microservices and designing APIs, with core proficiency in Python and Kotlin. Applied AI Engineering & MLOps: Direct experience implementing RAG, fine-tuning methodologies, and prompt engineering. Deep familiarity with LLM frameworks, vector databases, and MLOps tooling to deploy and update models safely. Data & Infrastructure Expertise: Practical knowledge of building data pipelines, managing feature stores, and structuring data for model consumption and evaluation. Hands-on experience with AWS, containerized services (Kubernetes, Docker), and cloud-native m