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N26, a digital banking platform headquartered in Berlin, is seeking a Senior Technical Product Manager to join the Intelligent Operations Platforms (IOP) segment. You will drive the development of AI solutions—spanning Machine Learning and Generative AI—that empower N26's teams and help the company lead the financial industry through technology.
In this role, you will partner strategically with senior product leadership to shape domain roadmaps aligned with N26's company-wide AI strategy while maintaining full autonomy over complex, high-stakes workstreams. You will focus on high-impact areas including automating back-office operations, enhancing real-time fraud and AML detection, and personalizing B2C customer growth and marketing journeys.
You will own the production AI lifecycle end-to-end for complex Machine Learning and GenAI initiatives—from initial hypothesis and data scoping through model evaluation, production deployment, and post-launch monitoring. You will act as a 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.
Balancing velocity with banking rigor, you will manage day-to-day delivery while upholding strict security, auditability, data privacy, and reliability standards required of a regulated bank. You will also empower autonomous AI adoption across N26 by defining standards, blueprints, and self-service toolkits that allow product squads to integrate AI capabilities safely and independently.
N26 operates across Europe with offices in Berlin, Vienna, and Barcelona, and a 1,500-person team representing more than 80 nationalities. The company offers a competitive personal development budget, work-from-home budget, fitness and wellness memberships, language apps, public transportation discounts, a relocation package with visa support, and an additional day of annual leave for each year of service.
REQUIREMENTS:
- 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 and Agentic Systems production experience: hands-on experience taking GenAI products from concept to live production, including direct experience building with 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; track record of building customer-facing or platform products powered by AI is required)
- B2C domain scale experience with technical background preferred: experience working within high-volume, highly scalable technical domains such as B2C FinTech, E-Commerce, or high-traffic consumer tech. 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
- Traditional ML foundation paired with modern LLM architecture expertise
- Deep technical literacy and ability to evaluate trade-offs between Large Language Models and deterministic ML/rule-based logic across cost, inference latency, explainability, and accuracy
- Data-driven and 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
- Strong collaboration skills within Agile environments, coupled with understanding of financial services constraints (GDPR, data residency, EU AI Act, risk governance, and security controls)
- Pragmatic action bias with focus on high-velocity execution and value creation
- Production reliability standards and uncompromising commitment to system resiliency
- Technical collaboration skills: comfortable speaking the same technical language as Principal Data Scientists and Platform Engineers while translating complex model behavior into clear updates for non-technical leadership
