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# Full Stack Engineer AI Systems

- **Company:** Ai-native
- **Location:** Spain
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Software Applications, Python (Programming Language), Node.Js, NoSQL, Open Source Technology, Next.js, SQL Databases, Pytorch, Large Language Models, Reliability of Systems, Backend, Kubernetes, Front End Software Development, Api Design, Docker
- **Published:** July 22, 2026
- **Apply:** https://www.jobleads.com/es/job/ea88a0dd46ee59a9dbd86043b876acd43

## About the Role

* Strong experience in full stack engineering (frontend + backend)
 * Solid understanding of system design and API architecture
 * Experience working with LLMs, RAG systems, or AI-powered applications
 * Ability to handle ambiguity and make pragmatic engineering decisions
 * Strong ownership - able to take features from idea to production
 * Comfort working in fast-moving environments with evolving requirements

## Description

We are looking for a Full Stack Engineer - AI Systems to build the product layer that turns these capabilities into usable, production-grade workflows. This includes designing how agents operate, fail, recover, and deliver consistent value to users., * Design agent workflows that handle planning, tool use, failure, and recovery across multiple steps.

 * Integrate LLMs, memory, and external tools into systems that behave reliably under real-world conditions
 * Design real-time AI interactions with streaming, partial results, and tight latency constraints
 * Improve system reliability, observability, and fallback mechanisms
 * Collaborate closely with ML, backend, and product teams to ship features end-to-end
 * Continuously iterate based on real usage and failure modes, * Own and ship AI-native product features that move beyond chat into persistent, goal-driven workflows
 * Design and deploy agent workflows that reliably complete multi-step tasks across tools and sessions
 * Reduce latency and improve responsiveness of AI interactions while maintaining output quality
 * Build robust fallback and recovery mechanisms for LLM and tool failures in production environments
 * Improve the success rate and reliability of AI-driven workflows through iteration, evaluation, and monitoring
 * Establish patterns and abstractions for integrating LLMs, memory, and external tools into scalable product systems
 * Contribute to a product experience where AI feels proactive, consistent, and dependable over time

Tech Stack

 * Next.js
 * Python
 * NodeJs
 * Pytorch
 * OpenAI / Anthropic / open-source LLMs
 * SQL & noSQL
 * Kubernetes
 * Docker


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