AI Engineer - Network

About this role

As an AI Engineer, you will design, build and maintain agentic AI solutions that power KPN’s move towards autonomous networks—creating intelligent agents that can observe, decide, and act to optimize performance in real time. You will collaborate closely with business stakeholders (across TDO and CM), domain experts, and engineering teams to develop scalable AI solutions and platforms to bring autonomous capabilities reliably into our operational processes.

Key daily responsibilities: Build, develop, and maintain agentic AI systems that enable autonomous network optimization and decision-making (e.g. the disruption broadband agent). Make system designs, validate software quality, check run-time behavior and focus on production ownership. Build and maintain scalable AI/ML pipelines with a strong focus on reliability, performance and maintainability. Define and implement monitoring, testing, and governance strategies to ensure robustness and compliance of agentic AI solutions in production.

Collaborate with network domain experts, data engineers, and software teams to integrate agentic solutions into real-time network environments. Translate network objectives into data-driven, self-adapting models using reinforcement learning and emerging AI tools. Stay up to date on innovations in agentic AI, reinforcement learning, and telecom network automation. You are eager to learn and can adapt quickly, both technically and personally.

You have strong analytical skills and take ownership of your work. You enjoy working with both technical and non-technical colleagues and can make complex ideas easy to understand. To be successful in this role, you bring: A strong background in software engineering (e.g. designing, building, and operating distributed systems in production). Solid experience with Python and/or Java, including writing clean, testable, and maintainable code.

Hands-on experience applying AI/ML techniques in production, not just experimentation (e.g. model deployment, monitoring, lifecycle management). Experience with AI/ML frameworks and the ability to integrate models into larger software systems. Understanding of end-to-end AI systems, including data pipelines, model serving, monitoring, and feedback loops. Familiarity with cloud-native architectures (e.g. containerization, CI/CD, scalable services) and building reliable platforms.