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# Expert AI Engineer

- **Company:** TUI
- **Location:** Spain
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, Amazon Web Services, Cloud Engineering, Continuous Integration, Data Governance, DevOps, Python (Programming Language), Knowledge Management, Machine Learning, PCI Data Security Standards, Tensorflow, Azure Machine Learning, Software Engineering, SQL Databases, Management of Software Versions, Enterprise Data Management, Data Processing, Data Ingestion, Pytorch, Large Language Models, Prompt Engineering, Generative AI, AI Platforms, Deployment Automation, HuggingFace, Machine Learning Operations, Service Stack
- **Published:** July 16, 2026
- **Apply:** https://es.trabajo.org/oferta-4009-0f3545b7d9f85455f1d84397d6b85240

## About the Role

Science, Mathematics, Engineering, or a related quantitative field, Deep understanding of probability, statistics, and the mathematical foundations of machine learning and optimization Proven experience building and deploying advanced AI systems, including Large Language Models (LLMs), multimodal, and generative AI architectures Exposure to agentic system design, retrieval-augmented generation (RAG) and prompt engineering techniques Strong proficiency in Python and experience with AI/ML development frameworks (e.G., PyTorch, TensorFlow, LangChain, Hugging Face or equivalent), with awareness that production environments may also rely on Java and/or Node.Js depending on the team's technology stack. Solid understanding of modern AI engineering practices, including model lifecycle management, observability, evaluation, versioning and continuous improvement Familiarity with AI solution delivery methodologies (e.G., CRISP-ML(Q), TDSP or modern agile ML lifecycles) Ability to
visualize, interpret, and communicate model outputs and insights effectively using modern tools and dashboards Specific technical requirements: Proven experience in architecting and implementing end-to-end AI/ML solutions --- from data ingestion and model training to deployment, monitoring and optimization Strong software engineering skills for AI system development, including data processing, API integration, and model serving (Python, SQL and optionally Java/Scala or similar) Hands-on experience with cloud-native AI platforms and services (AWS SageMaker, Azure ML, GCP Vertex AI or NVIDIA AI stack) - AWS as primary Proficiency in designing scalable ML/LLM pipelines and applying MLOps/LLMOps best practices (CI/CD, orchestration, monitoring, versioning, and deployment automation) Experience with diverse data modalities (structured, text, image, audio, video) and multimodal model integration Familiarity with handling complex data scenarios such as class imbalance, time-series
forecasting and anomaly detection Understanding of security, data governance and compliance considerations in AI system design Broad exposure to enterprise-scale AI solution design across industries such as BFSI, Healthcare, Aerospace, Manufacturing, Energy, Telecom or Technology sectors Proven ability to translate business and operational requirements into robust AI system architectures that deliver measurable impact Familiarity with challenges of deploying AI in regulated environments and ensuring compliance with data privacy and protection frameworks (e.G., GDPR, CCPA, PCI DSS) Experience managing sensitive or high-value data (PII, PHI), implementing strong security, governance and access control mechanisms Understanding of enterprise data ecosystems and integration patterns (CRM, ERP, knowledge management or workflow systems) Business-related requirements: Proven experience delivering production-grade AI solutions that achieve measurable business and operational outcomes
Strong ownership of the full AI engineering

## Description

teams at once) Primarily focus on developers, with potential to expand support to QA, BA, and DevOps over time Use and adapt to the approved internal toolset (e.G. Kiro, potentially Claude), ensuring compliance with TUI standards Collaborate with internal AI/innovation teams to address tooling gaps or improvement opportunities What Success Looks Like: Engineers are actively using AI in their daily work in a meaningful way AI is embedded into real development tasks (not just experimentation or training) Teams become more efficient through practical AI adoption Clear, reusable patterns for AI-supported development start to emerge Requirements We know that sometimes, you can't tick every box. We would still love to hear from you if you think you're a good fit General technical requirements: 8 years of professional experience in software, data, or AI engineering, including at least 3-4 years of hands-on experience designing and implementing AI/ML solutions, BSc, MSc, or PhD in Computer


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