Ingeniero de IA y ML - Agencia Europea en Zaragoza

Programming & Data Science: Python, R, Pandas, Scikit-learn, TensorFlow, PyTorch, SpaCy, NLTK.

AI & NLP: Machine Learning, NLP, recommendation systems, LLMs, RAG, Agentic AI, AI agents.

AI Frameworks: LangChain, LlamaIndex, Semantic Kernel.

Cloud & AI Platforms: AWS Bedrock, SageMaker, Azure AI Studio, Azure ML.

Data & Integration: SQL, NoSQL, Big Data, Data Lakes, web scraping, data ingestion, REST APIs, ETL.

MLOps & Engineering: MLflow, Kubeflow, containers, model lifecycle management, observability and CI/CD practices.

Responsible AI: AIF360, Fairlearn, SHAP, LIME, explainability, bias detection and mitigation, AI governance, data protection and AI Act compliance.

🔹What we offer🔹

⚡High-impact project

👫 Collaborative work environment

🚀 Career Plan

📘 Training Plan

💸 Flexible Remuneration

We are looking for an AI/ML Engineer to design, develop, and maintain Artificial Intelligence and Machine Learning solutions, with a strong focus on NLP, LLMs, agentic AI, and data-driven applications.

You will contribute to AI and Machine Learning activities within an international project supporting European institutions and agencies, developing intelligent solutions and data-driven services across a range of public-sector use cases.

AI & Machine Learning: Design, develop, and maintain Machine Learning solutions using modern frameworks and techniques, including predictive models, recommendation systems, NLP, LLMs, and Retrieval-Augmented Generation (RAG).

Generative & Agentic AI: Develop AI services, intelligent agents, and LLM-based applications using modern agentic AI frameworks and Python-based architectures.

Data Engineering: Support data ingestion, preparation, quality, governance, and lifecycle management across SQL, NoSQL, Big Data, and Data Lake environments.

Cloud & AI Platforms: Implement AI/ML solutions using cloud platforms and managed AI services, integrating models into scalable enterprise environments.

MLOps & Responsible AI: Apply MLOps practices throughout the model lifecycle, including experiment tracking, deployment, monitoring, observability, bias management, explainability, and compliance with data protection and AI governance requirements.

Technical Integration: Develop REST services and integrate AI capabilities into existing applications and processes, working collaboratively with technical and functional stakeholders.