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# AI Engineer

- **Company:** Abacum
- **Location:** Barcelona, Spain
- **Salary:** €60,000.0 - €80,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Big Data, Python (Programming Language), Machine Learning, Performance Tuning, Software Safety, Software Engineering, Large Language Models, Multi-Agent Systems, Prompt Engineering, Build Management, Information Technology, Virtual Agents
- **Published:** July 20, 2026
- **Apply:** https://www.adzuna.es/contact-us.html

## About the Role

+ Bachelor's degree in Computer Science or a related field.
 + Deep understanding of Large Language Models (LLMs) and their practical applications.
 + Proficiency in prompt engineering, chain-of-thought reasoning, context engineering, and token optimization techniques.
 + 5+ years of Software engineering background with expertise in Python and cloud platforms.
 + Hands-on experience with AI frameworks such as LangChain and LangGraph.
 + Self-motivated with the ability to work independently in a fast-paced, remote-first environment.
 + Experience with AI evaluation frameworks, benchmarking, and performance optimization.
Bonus

 + Strong experience with AI agent orchestration frameworks and architectures.
 + Background in machine learning or predictive modeling to derive insights from large datasets.
 + Knowledge of AI safety, alignment, and responsible AI development practices.
 + Practical experience working with Vector Databases (e.g., pgvector, Pinecone, Qdrant, etc) for RAG implementations.

## Description

+ Drive continuous innovation within AI Labs by researching cutting-edge technologies and experimenting with new PoCs.
 + Design and build sophisticated, multi-provider LLM workflows and autonomous agents capable of advanced reasoning and independent decision-making.
 + Building MCP (Model Context Protocol) agents to bridge the gap between LLMs and our proprietary data ecosystems.
 + Integrate AI capabilities into existing product workflows to automate and enhance core functionality.
 + Optimize AI agent performance through fine-tuning, evaluations, prompt optimization, and system design through RAG pipelines.


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