Key Skills

Job Description

Introduction 36 hours per week Start date: 30-09-2026 End date: 31-08-2027 Hybrid way of work ZZP is not allowed Relocation is not possible for this role AI Engineer for GenAI Assistants within Customer Service About the role We believe customer interactions are evolving toward AI agents that actively support personalized financial choices. These agents depend on high-quality enterprise data and knowledge to provide accurate, grounded, and reliable support. Preparing this information and systematically evaluating the quality of AI assistants are essential to creating valuable customer-service solutions. That is why we are looking for an AI Engineer to strengthen our Conversational AI teams. You will focus on preparing enterprise data and knowledge for advanced AI assistants and applying evaluation frameworks across single-turn, multi-turn, simulated, and agentic scenarios. In this role, you will help ensure that our assistants provide accurate, grounded, and reliable support to advisors. You will work in a hybrid setup, ideally spending one day a week onsite in Utrecht, collaborating closely with teams to implement new conversational and AI concepts. On Mondays, our department works from the office. For the rest of the week, we coordinate among ourselves whether we work from home or the office. The Digital & Customer Interaction Tribe aims to deliver an excellent customer experience, regardless of how the customer contacts Rabobank. Core values of our area include fun, collaboration, proactivity, and problem-solving. Your main focus will be on • Collaborate within a scrum team to prepare, structure, enrich, and validate enterprise data for AI knowledge bases and AI Chatbots. • Build and maintain data pipelines in Databricks to process structured and unstructured data for AI knowledge bases. • Apply evaluation frameworks to test advanced chatbots across single-turn, multi-turn, simulation-based, and agentic scenarios. • Prepare, clean, transform, and enrich source data to improve the quality and reliability of information used by AI assistants. • Create and maintain evaluation datasets covering realistic conversations, complex cases, edge cases, and multi-step workflows. • Implement automated testing, monitoring, and quality gates for changes to data, prompts, models, retrieval, and orchestration. • Deploy and manage cloud infrastructure using Infrastructure as Code, particularly Azure Bicep and CI/CD pipelines. • Analyse evaluation results and translate findings into improvements across knowledge, retrieval, prompts, tools, and application logic. Requirements Your Talent • A strong engineering and data-quality mindset, with an affinity for Agile development. • A proactive, hands-on approach to solving complex AI-quality and cloud-engineering challenges. • Strong critical thinking, ownership, and accountability for production-ready solutions. • Excellent communication skills and a customer-centric attitude. • A relevant HBO or university degree and fluency in English. Your Skillset • Strong proficiency in Python and experience with data engineering and automated testing. • Hands-on experience with Azure, Databricks, DevOps, CI/CD, and Infrastructure as Code, preferably Bicep. • Experience building data pipelines and preparing structured and unstructured enterprise data for AI knowledge bases and RAG applications. • Experience applying evaluation methods to advanced chatbots, including multi-turn, simulated, and agentic interactions. • Familiarity with Context Engineering, Agentic AI, Function Calling, retrieval strategies, and tool use.

Core Responsibilities

Prepare, structure, enrich, and validate enterprise data and build Databricks pipelines to support AI knowledge bases and customer-service assistants. Evaluate chatbot performance across single-turn, multi-turn, simulated, and agentic scenarios, then use the findings to improve data, retrieval, prompts, tools, and application logic.

Requirements

Requires a relevant higher professional or university degree, fluency in English, strong Python and data-engineering skills, and experience with automated testing. Candidates should also have hands-on experience with Azure, Databricks, DevOps, CI/CD, Infrastructure as Code, AI knowledge bases or RAG, and evaluation of advanced chatbots.