Principal AI Engineer - Conversational Banking

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.

What you'll do

• 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'll need

• 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 model serving platforms like SageMaker and Bedrock.
• Outcome-Driven Technical Leadership: A clear focus on turning technical complexity into tangible business outcomes, such as reducing financial crime investigation handling times and decreasing customer support escalation rates.