> Markdown version of [/jobs/ext/2032072-llm-application-engineer](https://www.wearedevelopers.com/jobs/ext/2032072-llm-application-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # LLM Application Engineer - **Company:** Piab Group - **Location:** Madrid, Spain - **Contract:** Permanent contract - **Skills:** Abstraction Layers, Application Programming Interfaces (APIs), Artificial Intelligence, Software Applications, Automated Storage and Retrieval Systems, Databases, Software Debugging, Distributed Systems, Python (Programming Language), Software Engineering, Pytorch, Large Language Models, Multi-Agent Systems, Generative AI, Backend, Virtual Agents, Automation Anywhere - **Published:** August 12, 2026 - **Apply:** https://www.adzuna.es/contact-us.html ## About the Role + Strong software engineering fundamentals with experience building AI-powered applications + Hands-on experience with LLMs, generative AI, or agent-based systems + Experience designing prompts, workflows, evaluations, or AI behaviour + Ability to write clean, production-quality code + Comfortable working across abstraction layers (model * system * product) + Strong problem-solving skills in ambiguous, fast-moving environments + Bias toward shipping, iteration, and continuous improvement Outcomes + AI features reach production quickly and deliver measurable user impact + LLM-powered workflows are reliable, scalable, observable, and maintainable + AI quality improves through systematic evaluation, experimentation, and iteration + AI workflows become increasingly predictable, efficient, and cost-effective + Complex AI capabilities are translated into simple, intuitive user experiences ## Description Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior. Our objective is to help users complete tasks daily enjoyable with over ~90%* reduced time. About the Role As an LLM Application Engineer, you will build the intelligence layer that powers A1's AI experiences. You will work at the intersection of LLMs, software engineering, and product - designing agent workflows, improving model behaviour, and turning AI capabilities into reliable user experiences. You will own problems end-to-end, from understanding user needs, designing Agentic workflows, integrating models and tools, building evaluation system and continuously improving AI behaviour in production. Focus + Build and ship LLM-powered applications and AI agent workflows + Design systems for reasoning, planning, memory, tool uuse and multi-step execution + Build reliable orchestration pipelines that turn probabilistic model outputs into predictable, observable, and safe actions + Integrate LLMs with APIs, databases, search, internal services, and external tools. + Develop prompting, context engineering, structured outputs, tool-calling, and other techniques to improve model behaviour + Build evaluation frameworks and datasets to measure AI quality, reliability, and regressions + Debug AI systems across the entire stack-from model behaviour and prompts to orchestration, backend services, and product UX + Optimise AI systems for quality, latency, and cost + Work closely with product and engineering teams to turn ambiguous product problems into working AI solutions + Establish production practices for observability, tracing, experimentation, evaluation, and continuous improvement Tech Stack + Python + LLM APIs and model providers, including OpenAI-compatible APIs and open-weight models + Agent frameworks and orchestration systems + Vector databases and retrieval systems + Backend services, APIs, and distributed systems + PyTorch / JAX ## Related Videos - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Kubernetes and Microservices with Multi-Model Databases](https://www.wearedevelopers.com/videos/382-kubernetes-and-microservices-with-multi-model-databases) - [LLMs in the wild: Building an AI agent that survives production](https://www.wearedevelopers.com/videos/100319-llms-in-the-wild-building-an-ai-agent-that-survives-production) - [Nest.js - TypeScript in the backend can also be clean](https://www.wearedevelopers.com/videos/1033-nest-js-typescript-in-the-backend-can-also-be-clean) - [Three years of putting LLMs into Software - Lessons learned](https://www.wearedevelopers.com/videos/1508-three-years-of-putting-llms-into-software-lessons-learned) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [How to Use Generative AI to Accelerate Learning to Code](https://www.wearedevelopers.com/magazine/530-how-to-use-generative-ai-to-accelerate-learning-to-code)