Landbot is an AI Agent Builder that enables companies to design and deploy conversational experiences across multiple channels.
Our platform empowers teams to build tailored AI agents that can understand context, reason, make decisions, and act — responding like the best employee in the company, or even better, the best possible one for each customer and use case.
We work with companies across Lead Generation, Sales, Customer Support, and GTM operations, helping them move from scripted chatbots to intelligent, autonomous agents that deliver real business impact.
Landbot is product-driven, engineering-led, and globally distributed, with a strong focus on quality, ownership, and long-term thinking.
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Human-first: Empathy, respect, and diversity as drivers of innovation.
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Unity: Trust, psychological safety, and continuous improvement as teams of teams.
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Adaptability: Curiosity, constant learning, and just enough process to stay agile.
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Customer-centric: Value first; progress measured by real impact, not vanity metrics.
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Collaboration: One team, shared decisions, and early draft culture.
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Trust & commitment: Open debate, then full support once decisions are made.
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Challenging mindset: Question assumptions and the status quo to keep evolving.
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You’ll work closely with backend, AI, product, and design teammates.
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Small, senior teams with high ownership and autonomy.
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Strong focus on impact, quality, and learning.
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.
At Landbot, we believe conversational AI should feel less like software and more like working with the best person in the company — or even better, the best possible person for every question, every customer, and every moment.
This role is not about demos or prototypes. It’s about turning that vision into reliable, scalable, production-grade systems that power real customer interactions at scale.
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Co-define Landbot’s technical vision for agentic systems: autonomous agents, multi-agent collaboration, planning, decision-making, and execution.
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Translate high-level product and business problems into concrete agentic architectures and system designs.
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Act as a technical reference for agentic patterns, trade-offs, and best practices across teams.
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Design and build production-grade agentic systems
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Context engineering and memory strategies
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Tool use and function calling
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Planning, reasoning, and decision-making loops
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Multi-agent collaboration and coordination
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Integrate LLMs into backend systems with clear APIs, contracts, and failure modes.
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Evaluation, reliability, and observability
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Design and implement LLM evaluation systems
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Instrument agentic systems with strong observability: tracing, logging, debugging, and cost monitoring.
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Build guardrails, safety mechanisms, and fallback strategies to ensure predictable behavior in production.
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Backend engineering excellence:
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Design scalable backend architectures to support AI workloads at scale.
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You’re comfortable working with complex backend stacks and architectural approaches (e.g., Domain-Driven Design), knowing when to leverage them to bring clarity and scalability—and when to avoid dogmatic application that slows delivery.
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Build and maintain APIs, services, and data pipelines that power AI agents.
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Make thoughtful trade-offs between performance, reliability, cost, and speed of iteration.
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Ensure systems are testable, maintainable, and secure.
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You have a strong background in developing and maintaining decoupled backend systems
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Mentor and guide other engineers working on AI and backend systems.
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Participate in architectural reviews and help raise the bar for engineering quality.
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Collaborate closely with Product, Data, and Design to turn ideas into shipped value.
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.
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Proven hands-on experience shipping LLM-powered features or systems to production, beyond prototypes or internal demos.
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Experience with modern LLMs and providers (OpenAI, Anthropic, Google, and/or open-source models), including their trade-offs in quality, latency, and cost.
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Hands-on experience with Agentic architectures and workflows, Context Engineering and Retrieval-Augmented Generation (RAG).
Practical understanding of the full lifecycle: experimentation evaluation deployment- monitoring.
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Experience designing evaluation frameworks for LLM systems.
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Comfortable operating in ambiguity and shaping problems, not just solving predefined tasks.
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Product-minded: you care about real users and real outcomes.
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You are fluent in English and Spanish.
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Eligibility to work in Spain.
Hiring Process1️⃣ HR interview (30 min) - We know each other, we know your concerns, validate details and experience and we tell you more about Landbot2️⃣ Interview with Engineering Director (45 min) - Initial interview to get to know each other. We will check your background and product mindset and you will see if we are a good fit for you!3️⃣ Interview with the team (120 min) - You'll meet members of our team to go deeper into your technical experience, how you approach problem-solving, system design and architecture decisions, as well as how you collaborate with others to deliver solutions. It's a two-way conversation, so feel free to ask about our challenges, tech stack, and ways of working.4️⃣ Meet the Founders (30 min) - Final conversation to ensure alignment on vision and values.
Benefits
Hybrid work model: flexibility to work remotely, from our Barcelona office - ️, or a combination of both.
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Collaborative work environment.
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Flexible working hours.
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Paid time off and flexible holidays: 26 paid days per year (23 regular days + December 24th & 31st) , plus one additional day off on your birthday.
Annual budget for training and professional development - .
Transportation ticket - .
English and Spanish lessons - .
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Flexible compensation plan through Cobee.
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Team-building activities.
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Referral program with bonuses for bringing in talented professionals.
Please submit your résumé along with a cover letter explaining why you are the ideal candidate for Landbot and this role. We look forward to hearing from you!
