Senior Back end Engineer

For Rabobank Utrecht, we are looking for a Senior Back-end engineer Conversational AI. We believe customer interactions are evolving towards AI agents that actively support personalized financial choices. These agents do not operate in isolation: they collaborate with other systems, services, and agents to reason, act, and deliver outcomes at the right moment. Screens and integrations remain essential, but their role shifts, from static interaction points to dynamic execution and orchestration layers that expose insights, actions, and context exactly when needed.

That’s why we are looking for a Senior Backend Engineer to strengthen our Conversational AI teams and accelerate the delivery of high-impact AI-powered features for our AI Agent Robin. This includes leading backend initiatives focused on integrating AI agents with existing services, enabling secure context sharing, and supporting agent-to-agent (A2A) interactions and Model Context Protocol (MCP)-style patterns to create richer, more capable customer conversations.

You’ll work in a hybrid setup, ideally spending one day a week onsite in Utrecht, collaborating closely with teams to implement new conversational and agent-integration 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:

• Designing and building backend services that power key conversational features for our AI agent Robin (e.g., financial insights, mortgage scenarios, transaction explanations, and document generation), with a strong focus on AI-agent-driven workflows;
• Integrating Large Language Models (LLMs) and AI agents with internal systems using well-defined APIs, event-driven patterns, and secure context propagation;
• Enabling agent orchestration and A2A-style interactions, allowing Robin to collaborate with other agents or services (e.g., planning, reasoning, and execution agents) using MCP-inspired patterns;
• Developing reusable backend components and services that can be leveraged across multiple conversational agents, ensuring the platform is scalable, observable, and future-proof;
• Working closely with front-end, integration, and platform engineers to ensure seamless data, intent, and context flow between the chat interface, AI agents, and core banking systems;
• Collaborating with business analysts, designers, data scientists, and fellow engineers to shape conversational capabilities, define backend best practices, and decide how responsibilities are split between agents, services, and traditional application logic.

No zzp

Your Talent:

• Available from as soon as possible until 31-03-2027 for 36 hours per week
• Senior-level experience in backend software engineering, you have 5 years of professional experience building and operating production-grade services;
• Hands-on experience integrating AI/LLM-based components into backend systems, including prompt orchestration, tool/function calling, and context management;
• Experience with distributed systems and API design, and familiarity with event-driven or message-based architectures;
• Ideally, you have previously worked at Rabobank and are familiar with our backend and integration landscape;
• A strong sense of ownership – you’re proactive, quality-driven, and comfortable making architectural trade-offs in complex environments;
• A customer- and outcome-focused mindset, you understand that backend design directly impacts customer trust, explainability, and experience.

Your Skillset:

• Backend development using Python and Java;
• Designing and implementing REST APIs, MCP and A2A integrations for AI-agents and applications;
• Experience with low-code conversational platforms like copilot studio is a pre;
• Experience with AI-agent architectures, including tool invocation, context persistence, conversation state, and guardrails;
• Event-driven architectures, messaging platforms, and distributed system patterns;
• Security, privacy, and compliance considerations when exposing data to AI agents;
• Observability (logging, metrics, tracing) and reliability engineering in AI-augmented systems.