Principal AI Engineer
N26Mobile Banking company
Berlin, GermanyLead
Coatue Management
Dragoneer Investment Group
Tencent
Valar Ventures
Third Point Ventures
Allianz X
Data & AI
About the role
TL;DR
Architect next-gen intelligent conversational ecosystems and automated backend intelligence.
- •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.
- •Key Responsibilities 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.
- •Productionize Complex AI Prototypes: Act as the primary technical bridge transforming data science prototypes into customer-facing features.
- •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.
- •Requirements 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.
Required skills
PythonKotlinAWSKubernetesDockerSageMakerAnthropic APILLMsMLflowKubeflowVertex AIONNXJAXPostgreSQLMySQL
Domain expertise
fintech
Tech stack
PythonKotlinAWSKubernetesDockerSageMakerAnthropic APILLMsMLflowKubeflowVertex AIONNXJAXPostgreSQLMySQLMongoDBRedisElasticsearchDynamoDBCassandraSQLiteOracleSQL ServerNeo4jCockroachDBTimescaleDBInfluxDBSupabaseFirebasePrisma