Senior Technical Product Manager — GenAI & Agentic Systems

<h1>About the opportunity</h1>

<p>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.</p>

<p>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.</p>

<h3><strong>In this role, you will:</strong></h3>

<ul>

<li><span style="font-weight: bold;">Partner Strategically with Senior Product Leadership:</span> 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.</li>

<li><span style="font-weight: bold;">Drive Measurable Business Impact:</span> 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.</li>

<li><span style="font-weight: bold;">Own the Production AI Lifecycle:</span> 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.</li>

<li><span style="font-weight: bold;">Bridge Engineering and Business Strategy:</span> 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.</li>

<li><span style="font-weight: bold;">Balance Velocity with Banking Rigor:</span> 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.</li>

<li><span style="font-weight: bold;">Empower Autonomous AI Adoption:</span> 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.</li>

</ul>

<h3><strong>What you need to be successful:</strong></h3>

<h4><strong>Background:</strong></h4>

<ul>

<li>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.</li>

<li><span style="font-weight: bold;">Real-World GenAI &amp; Agentic Systems Production Experience:</span> 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).</li>

<li><span style="font-weight: bold;">B2C Domain Scale (Technical Background Preferred):</span> 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.</li>

<li><span style="font-weight: bold;">Full AI/ML Specialization:</span> 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.</li>

</ul>

<h4><strong>Skills:</strong></h4>

<ul>

<li><span style="font-weight: bold;">Traditional ML Foundation + GenAI Evolution:</span> A background grounded in traditional Machine Learning methodologies (classification, regression, ranking/recommendation engines) paired with hands-on expertise in modern LLM architecture.</li>

<li><span style="font-weight: bold;">Deep Technical Literacy &amp; Architecture Evaluation:</span> 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.</li>

<li><span style="font-weight: bold;">Data-Driven &amp; Model Evaluation Mindset:</span> Proficiency in defining, tracking, and translating offline model evaluation metrics (precision, recall, F1, confidence thresholds, hallucination rates) into tangible business KPIs and operational ROI.</li>

<li><span style="font-weight: bold;">Cross-Functional &amp; Regulatory Awareness:</span> 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).</li>

</ul>

<h4><strong>Traits:</strong></h4>

<ul>

<li><span style="font-weight: bold;">Pragmatic Action Bias:</span> A focus on high-velocity execution and value creation, knowing how to balance model perfection with pragmatic, incremental shipping in a fast-paced environment.</li>

<li><span style="font-weight: bold;">Production Reliability:</span> Uncompromising standards for system resiliency, ensuring all AI-driven platform features are robust enough for a global banking infrastructure.</li>

<li><span style="font-weight: bold;">Technical Collaboration:</span> Comfortable speaking the same technical languag