Jobs / Germany / N26

Senior Technical Product Manager — GenAI & Agentic Systems

N26 · 🌍 Berlin

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  • No government sponsor record hereThis employer posted directly and does not match a government sponsor register.
  • The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
  • Can’t check pay against the visa rulesNo salary stated. Germany EU Blue Card needs at least €50,700 a year (shortage-occupation / recent-graduate: €45,934). Source: https://www.make-it-in-germany.com/en/visa-residence/types/eu-blue-card, rules effective 2026-01-01.
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About the role

About the opportunity We are seeking a seasoned Senior Product Manager to join the “Intelligent Operations Platforms” (IOP) segment. In this role, you will drive the development of AI solutions - spanning both Machine Learning and Generative AI - that empower our teams and help N26 lead the financial industry through technology. Our AI Products team acts as a central hub of innovation. We don't just build features; we build and manage AI capabilities that allow every other part of the bank - from Financial Crime and Risk to Growth and Marketing - to become smarter. You will work at the intersection of cutting-edge technology and real-world banking, turning sophisticated models into scalable products that improve efficiency, compliance and the customer experience. In this role, you will: • Partner Strategically with Senior Product Leadership: Work closely with cross-functional PM leads to shape and align your domain roadmap with N26’s broader company-wide AI strategy, maintaining full autonomy over complex, high-stakes workstreams. • Drive Measurable Business Impact: Focus on high-impact areas, including automating back-office Operations, enhancing real-time fraud and AML detection (FinCrime), and personalizing B2C customer growth and marketing journeys. • Own the Production AI Lifecycle: Lead end to end delivery for complex Machine Learning and GenAI initiatives—moving from initial hypothesis, data scoping, and model evaluation harnesses to production deployment and post-launch monitoring. • Bridge Engineering and Business Strategy: Act as the technical sparring partner for Data Scientists and ML Engineers, translating business constraints into model requirements and ensuring technical architectures solve real consumer and operational problems. • Balance Velocity with Banking Rigor: Manage day-to-day delivery, maintaining a healthy balance between launching innovative AI capabilities and upholding the strict security, auditability, data privacy, and reliability standards expected of a regulated bank. • Empower Autonomous AI Adoption: Act as an AI evangelist and platform owner by defining standards, blueprints, and self-service toolkits that allow product squads across N26 to integrate AI capabilities into their own features safely and independently. What you need to be successful: Background: • 5+ years of Product Management experience in a technology-driven environment, with a proven track record of owning and shipping production-grade AI/ML capabilities. • Real-World GenAI & Agentic Systems Production Experience: Hands-on experience taking GenAI products from concept to live production. You must have direct experience building with concepts like Retrieval-Augmented Generation (RAG), Agentic workflows (ReAct framework, state/memory management, function-calling), and multi-agent orchestration systems. (Note: Utilizing off-the-shelf AI productivity tools is not sufficient; we require a track record of building customer-facing or platform products powered by AI). • B2C Domain Scale (Technical Background Preferred): Experience working within high-volume, highly scalable technical domains (such as B2C FinTech, E-Commerce, or high-traffic consumer tech). Note: Pure B2B background with low customer scale does not fit the high-volume requirements of this role. • Full AI/ML Specialization: Comprehensive expertise navigating the full AI product lifecycle, including problem scoping, target metric definition, data ingestion/labeling, model training/fine-tuning, evaluation framework design, deployment, latency optimization, and continuous retraining. Skills: • Traditional ML Foundation + GenAI Evolution: A background grounded in traditional Machine Learning methodologies (classification, regression, ranking/recommendation eng

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Source: Greenhouse (employer board) First seen: 2026-10-01 Last confirmed: 2026-10-03 How our data works → Report this job

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