Ai Engineer

Swiss Re - Madrid, M, ES

AI ENGINEERRegular EmploymentApply now ShareLocation: Madrid, M, ES About the Team and Our Scope We are a forward-thinking tech organization within Swiss Re, delivering transformative AI/ML solutions that redefine how businesses operate. Our mission is to build intelligent, secure, and scalable systems that deliver real-time insights, automation, and high-impact user experiences to clients globally. You'll join a high-velocity AI/ML team working closely with product managers, architects, and engineers to create next-gen enterprise-grade solutions. Our team is built on a startup mindset — bias to action, fast iterations, and ruthless focus on value delivery. We’re not only shaping the future of AI in business — we’re shaping the future of talent. This role is ideal for someone passionate about advanced AI engineering today and curious about evolving into a product leadership role tomorrow. Role Overview As an AI/ML Engineer, you will play a pivotal role in the research, development, and deployment of next-generation GenAI and machine learning solutions. Your scope will go beyond retrieval-augmented generation (RAG) to include areas such as prompt engineering, long-context LLM orchestration, multi-modal model integration (voice, text, image, PDF), and agent-based workflows. You will help assess trade-offs between RAG and context-native strategies, explore hybrid techniques, and build intelligent pipelines that blend structured and unstructured data. You’ll work with technologies such as LLMs, vector databases, orchestration frameworks, prompt chaining libraries, and embedding models, embedding intelligence into complex, business-critical systems. This role sits at the intersection of rapid GenAI prototyping and rigorous enterprise deployment, giving you hands-on influence over both the technical stack and the emerging product direction. Key Responsibilities Build Next-Gen GenAI Pipelines: Design, implement, and optimize pipelines across RAG, prompt engineering, long-context input handling, and multi-modal processing. Prototype, Validate, Deploy: Rapidly test ideas through PoCs, validate performance against real-world business use cases, and industrialize successful patterns. Ingest, Enrich, Embed: Construct ingestion workflows including OCR, chunking, embeddings, and indexing into vector databases to unlock unstructured data. Integrate Seamlessly: Embed GenAI services into critical business workflows, balancing scalability, compliance, latency, and observability. Explore Hybrid Strategies: Combine RAG with context-native models, retrieval mechanisms, and agentic reasoning to build robust hybrid architectures. Drive Impact with Product Thinking: Collaborate with product managers and UX designers to shape user-centric solutions and understand business context. Ensure Enterprise-Grade Quality: Deliver solutions that ...