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AI Engineer | Generative AI & RAG Seville + B2 English or 100% Remote (M/F/X)

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Description

About the role

Location: Seville or 100% remote | Direct hire

Are you passionate about Generative Artificial Intelligence and do you want to develop solutions that transform the way organizations use their data?

At Experis IT we are looking for an AI Engineer with experience in LLMs and RAG architectures to join an international technology company directly, participating in innovative AI, data, and cloud projects.

What you'll do

  • Design and develop data and Artificial Intelligence solutions in cloud and on-premise environments.

  • Integrate LLMs with corporate information to create intelligent assistants.

  • Implement RAG architectures using LangChain, LlamaIndex, or equivalent frameworks.

  • Develop AI applications using Python, APIs, and systems integration.

  • Optimize the accuracy, quality, and reliability of generated responses.

  • Collaborate with architecture, data, and business teams on scalable and secure solutions.

What we're looking for

  • STEM university degree (MECES 2) or equivalent experience.

  • At least 4 years of experience as an AI Engineer, Data Scientist, or similar.

  • Hands-on experience in generative AI, LLMs, and RAG architectures.

  • Proficiency in Python, APIs, and AI solution development.

  • Knowledge of cloud technologies and generative AI services.

  • Autonomy, analytical thinking, and an orientation toward collaborative work.

Nice to have

  • Advanced English is valued.

What you'll get

  • Direct hire and long-term technological project.

  • Remote or on-site modality in Seville.

  • Intensive working hours on Fridays and during the summer.

  • Participation in AI projects with real business impact.

At Experis IT we connect technological talent with opportunities that drive their professional development, betting on specialization and growth in cutting-edge technologies.

Ready to take generative AI to the next level?

We want to meet you!

Skills

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See also

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