Join Landbot, a leading conversational AI company, as an AI Engineer specializing in agentic systems and backend development. In this role, you will co-define Landbot's technical vision for autonomous agents, multi-agent collaboration, planning, decision-making, and execution. You will design and build production-grade agentic systems, integrate LLMs into backend systems, and ensure system reliability and observability. You will also mentor and guide other engineers, participate in architectural reviews, and collaborate closely with Product, Data, and Design teams. Enjoy a flexible work environment in sunny Barcelona with various benefits.
About the role
- We are looking for a senior-level engineer who combines strong backend engineering expertise with hands-on experience building LLM-powered systems, to help us define, design, and build the next generation of agentic AI systems at Landbot - We are especially interested in engineers who have grown from a strong backend background and have progressively specialised in applied AI and LLM-based systems - Proven hands-on experience shipping LLM-powered features or systems to production, beyond prototypes or internal demos - Product-minded: you care about real users and real outcomes - Hands-on experience with Agentic architectures and workflows, Context Engineering and Retrieval-Augmented Generation (RAG) - Comfortable operating in ambiguity and shaping problems, not just solving predefined tasks - Eligibility to work in Spain - Practical understanding of the full lifecycle: experimentation → evaluation → deployment → monitoring - Experience designing evaluation frameworks for LLM systems - Experience with modern LLMs and providers (OpenAI, Anthropic, Google, and/or open-source models), including their trade-offs in quality, latency, and cost - You are fluent in English and SpanishKey missions
- Co-definir la visión técnica de Landbot para sistemas agenticos, incluyendo agentes autónomos, colaboración entre múltiples agentes, planificación, toma de decisiones y ejecución.
- Diseñar y construir sistemas agenticos de producción, incluyendo la ingeniería de contexto y estrategias de memoria, el uso de herramientas y la llamada a funciones.
- Integrar modelos de lenguaje grande (LLMs) en sistemas backend con APIs claras, contratos y modos de falla.