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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # 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, Information Technology, Deployment Automation, HuggingFace, Machine Learning Operations, Service Stack - **Published:** July 16, 2026 - **Apply:** https://es.trabajo.org/oferta-4009-83eb9c9c395464a23bac3c7738b60be7 ## About the Role Ciklum is looking for an Recuerde revisar su CV antes de enviar la solicitud. Además, asegúrese de leer todos los requisitos relacionados con este puesto. Expert AI Engineer to join our team in Spain. We are a custom product engineering company that supports both multinational organizations and scaling startups to solve their most complex business challenges. With a global team of over 4,000 highly skilled developers, consultants, analysts and product owners, we engineer technology that redefines industries and shapes the way people live. About The Role As an Expert AI Engineer, you'll become a part of a cross-functional development team engineering experiences of tomorrow. Responsibilities Embed into product teams and work 1:1 with senior engineers on real tasks Co-develop and refine ways of using AI in everyday engineering workflows Help teams adopt "agentic" ways of working through practical application, not just guidance Start with one developer per team (phased rollout, not all 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 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 sys ## Related Videos - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [Agentic employees in world's most downloaded FinTech app](https://www.wearedevelopers.com/videos/100123-agentic-employees-in-world-s-most-downloaded-fintech-app) - [DevOps Maturity Check – a way to balance autonomy and alignment](https://www.wearedevelopers.com/videos/58-devops-maturity-check-a-way-to-balance-autonomy-and-alignment) - [Overview of Machine Learning in Python](https://www.wearedevelopers.com/videos/840-overview-of-machine-learning-in-python) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)