We are looking for an

AI Engineer to join a dynamic AI team within an international technology environment. In this role, you will design, build, and deploy

LLM-powered applications, RAG pipelines, multi-agent systems, and scalable AI solutions on AWS. You will work at the intersection of software engineering, AI engineering, data pipelines, and cloud deployment , contributing to real use cases across the group. This role is highly hands-on and focused on building intelligent systems that move from experimentation to production.

You will work closely with Data Scientists, Platform and DevOps Engineers, Data Engineers, domain experts, Product Managers, and the Tech Lead to move AI solutions from experimentation into production. If you enjoy working with

LLMs, agents, RAG, vector databases, Python APIs, and AWS-native AI services , this could be a great fit. What you’ll do

Design, build, and maintain

LLM-powered applications and multi-agent systems primarily using

LangGraph and

LangChain , alongside tools such as CrewAI or similar frameworks Develop and optimise

RAG pipelines , including document ingestion, chunking strategies, embedding generation, retrieval logic, and vector search Implement and manage vector databases such as pgvector on Aurora, OpenSearch, Pinecone , or similar Build and maintain data and ETL pipelines using

Apache Airflow, Prefect , or similar tools Develop backend services and APIs in

Python / FastAPI to serve AI models, RAG systems, and agent workflows Deploy and manage AI workloads on

AWS services such as

Bedrock, SageMaker, Lambda, S3, Aurora/RDS, EC2 Work with

Docker and Kubernetes to containerise and orchestrate AI workloads Design and execute evaluation frameworks for LLM outputs, including automated testing, LLM-as-judge approaches, and human-in-the-loop review Work with LLM APIs and orchestration tools such as

AWS Bedrock, OpenAI API, Anthropic API , or similar Apply prompt engineering and LLM evaluation methodologies,



and assess when fine-tuning or other adaptation techniques are appropriate. Collaborate with domain experts, Data Engineers, Product Managers, and the Tech Lead to turn business requirements into AI solutions

Participate in Scrum ceremonies and contribute to a collaborative Agile engineering culture Stay up to date with the rapidly evolving AI/ML ecosystem and proactively propose new tools, improvements, and approaches Support less experienced team members and share AI engineering best practices across the team.

3–5 years of experience in Software Engineering , with at least

1–2 years focused on AI / ML Engineering Strong proficiency in

Python Experience with AI/ML and LLM frameworks such as

LangChain, LangGraph, Hugging Face, PyTorch , or similar Hands-on experience building

RAG systems , including embeddings, vector stores, semantic search, and hybrid search strategies Experience working with

LLM APIs such as

AWS Bedrock, OpenAI API, Anthropic API , or similar Solid understanding of prompt engineering, fine-tuning techniques, and LLM evaluation methodologies Hands-on experience with

AWS services such as

EC2, S3, Lambda, Aurora/RDS, Bedrock, SageMaker Experience with observability and evaluation platforms for LLMs such as

Langfuse, Datadog LLM Observability, LangSmith Experience with

Docker and Kubernetes Familiarity with data pipeline tools such as

Apache Airflow, Prefect , or similar Experience developing backend services or APIs, ideally with

FastAPI Proficiency with

Git and software engineering best practices Experience working in a





Scrum Agile environment Strong problem-solving, analytical thinking, communication, and teamwork skills Experience with multi-agent architectures and protocols such as

A2A or MCP Familiarity with MLOps practices: model versioning, experiment tracking, MLflow, Weights & Biases, and CI/CD for ML Knowledge of graph databases or knowledge graphs for enhanced retrieval Experience with CI/CD pipelines using tools such as

GitHub Actions Familiarity with Infrastructure as Code, especially

Terraform Experience with code quality and security tools such as

SonarCloud, Snyk Experience in aviation, travel, or large-scale digital environments Spanish language skills are a plus Hybrid model - 2 days onsite per week Why join this project?

People first – diverse and inclusive culture in an international environment. Build production-ready LLM applications, RAG systems, and agentic AI solutions Work with cutting-edge AI technologies across the LLM, agents, vector search, and AWS ecosystem Contribute to scalable engineering practices around AI applications, data pipelines, evaluation, and deployment Gain hands-on exposure to AWS-native AI services such as Bedrock, SageMaker, Lambda, S3, and Aurora Be part of a fast-moving AI environment where experimentation, ownership, and impact are highly valued High team stability and collaborative culture. €1200 per year training budget and continuous learning opportunities. Private health insurance and benefits package. Flexible working hours and hybrid model.

Wellhub: fitness, wellness, and mental health support. Football and paddle tennis teams sponsored by Capitole. Team buildings, integral events, and strong tech communities.

Information Security Notice

The employee will have access to confidential information related to Capitole and the assigned project. Compliance with internal security and information protection policies is mandatory.

📌 AI Engineer (Madrid)
🏢 Capitole
📍 Madrid