Ai Engineer

Capitole; Barcelona; Híbrido; EUR 55.000 - 75.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Hybrid work model (2 days onsite per週)

Private health insurance

€1200 per year training budget

Team buildings and events

Wellhub: fitness, wellness and mental

Flexible working hours

Descripción de la vacante

Capitole in an international tech environment seeks an AI Engineer to design, build, and deploy LLM-powered apps, RAG pipelines, and multi‑agent systems on AWS.

You will collaborate with Data Scientists, Platform/DevOps, and Product Managers to move AI solutions from experimentation to production using Python, FastAPI, Docker, and Kubernetes.

Hybrid work model (2 days onsite weekly) with a focus on scalable AI, evaluation frameworks, and strong engineering practices.

Formación

• 3–5 years software engineering with 1–2 years in AI/ML
• Strong Python proficiency
• Experience with LangChain/LangGraph and ML frameworks
• Hands-on RAG systems and vector stores
• Experience with LLM APIs such as AWS Bedrock/OpenAI/Anthropic
• Knowledge of prompt engineering and evaluation
• Experience with Docker and Kubernetes
• Experience with Airflow/Prefect
• Experience shipping production AI solutions
• Spanish language skills a plus

Responsabilidades

• Design, build, and maintain LLM-powered applications and multi-agent systems using LangGraph/LangChain
• Develop and optimise RAG pipelines, embeddings, and retrieval logic
• Implement and manage vector databases on Aurora/OpenSearch/Pinecone
• Build and maintain data and ETL pipelines with Airflow/Prefect
• Develop backend services and APIs in Python/FastAPI to serve AI models and workflows
• Deploy and manage AI workloads on AWS Bedrock, SageMaker, Lambda, S3, Aurora/RDS
• Work with Docker and Kubernetes to containerise and orchestrate workloads
• Collaborate with domain experts and product managers to turn business needs into AI solutions
• Participate in Scrum ceremonies and contribute to Agile culture
• Stay up to date with AI/ML ecosystem and propose improvements

Conocimientos

Python

LangChain

LangGraph

Hugging Face

PyTorch

AWS Bedrock

RAG pipelines

Docker

Kubernetes

FastAPI

Herramientas

CrewAI

pgvector

Aurora

OpenSearch

Pinecone

Apache Airflow

Prefect

FastAPI

Docker

Descripción del empleo

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.

Must Have

• 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

Nice to Have

• 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.
• Flexible compensation model.
• 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, global 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.