Senior Technical Product Manager — GenAI & Agentic Systems

N26 · Barcelona

  • Senior
  • Full-time
  • Posted 2026-09-11
  • Confirmed live on 25 September 2026

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Job description

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 engines) paired with hands-on expertise in modern LLM architecture.

• Deep Technical Literacy & Architecture Evaluation: Strong grasp of AI technical fundamentals, with the ability to evaluate trade-offs between Large Language Models (LLMs) and deterministic ML/rule-based logic across cost, inference latency, explainability, and accuracy.

• Data-Driven & Model Evaluation Mindset: Proficiency in defining, tracking, and translating offline model evaluation metrics (precision, recall, F1, confidence thresholds, hallucination rates) into tangible business KPIs and operational ROI.

• Cross-Functional & Regulatory Awareness: Strong collaboration skills within Agile environments, coupled with an understanding of financial services constraints (GDPR, data residency, EU AI Act, risk governance, and security controls).

Traits:

• Pragmatic Action Bias: A focus on high-velocity execution and value creation, knowing how to balance model perfection with pragmatic, incremental shipping in a fast-paced environment.

• Production Reliability: Uncompromising standards for system resiliency, ensuring all AI-driven platform features are robust enough for a global banking infrastructure.

• Technical

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