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Santander AI Lab - Frontier Agentic AI EngineerSantander · SANTA BARBARA 1
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Santander AI Lab - Frontier Agentic AI Engineer

🗓️ Posted 2026-09-24 SANTA BARBARA 1 Hybrid ICT
💰 Salary Not shared 📊 Market 3,500 EUR–5,500 EUR/mo

About the Company

ace a strong risk culture and all our professionals at all levels are expected to take a proactive and responsible approach toward risk management. Our Chief Data & Artificial Intelligence Officer (CDAIO) division is building a world-class AI & Data team to make a difference in the lives of over 170 million people worldwide, through one of the largest banks in the world. We are undergoing one of the biggest transformations in our history and technology is at the heart of our strategy. Join our team to play a part in one of the most important technological projects for the financial sector in the world. THE DIFFERENCE YOU MAKE Santander AI Lab (CDAIO) is looking for an Frontier Agentic AI Engineer based out of Madrid, Spain. The AI Lab is the applied innovation engine of one of the world's largest banks. We detect emerging opportunities, build working prototypes, validate them with real data, and transfer them to scale. We work with Anthropic, Sakana AI, AWS, ICMAT, CMU, INRIA and other world-class partners. Our published research (arXiv:2602.14606, arXiv:2602.02170) sets the formal foundation for everything we build. We're shaping the way we work through innovation, cutting-edge technology, collaboration and the freedom to explore new ideas. To succeed in this role, you will be responsible for: Designing, building and deploying production-grade agentic AI systems - multi-agent orchestration with real memory, planning, tool use and error recovery. Not

Requirements

  • ● Our people are our greatest strength. Every individual contributes unique perspectives that make us stronger as a team and as an organization. We're enabling teams to go beyond by valuing who they are and empowering what they bring.
  • ● The following requirements represent the knowledge, skills, and abilities essential for success in this role. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
  • ● Professional Experience
  • ● -8 years of software engineering or AI engineering experience , with at least 2 years building and maintaining LLM-powered systems in production environments - not in notebooks. (Required)
  • ● Demonstrated hands-on experience designing and deploying multi-agent AI systems with real-world complexity : memory management, stateful orchestration, tool use, multi-step planning, and graceful failure recovery. (Required)
  • ● Track record of delivering complete systems independently within tight timelines - from architecture decision to production-ready code. Portfolio of real systems, not just demos. (Required)
  • ● Prior experience in banking, fintech, or other regulated industries. (Preferred)
  • ● Exposure to Harness Engineering methodologies: spec-driven development, AI-assisted software creation at scale. (Preferred)
  • ● Bachelor's or Master's degree in Computer Science, Software Engineering, Mathematics, Physics, or equivalent technical field. (Required)
  • ● Master's degree or equivalent advanced qualification in AI, Machine Learning or related discipline. (Preferred)
  • ● English: professional working proficiency - all technical documentation, papers and partner communications are in English. (Required)
  • ● Spanish: professional working proficiency - day-to-day team communication and stakeholder collaboration. (Required)
  • ● Python: advanced proficiency. Clean, tested, production-grade code. You know the difference between a script and a system. (Required)
  • ● Agentic AI frameworks: LangGraph, AutoGen, CrewAI, or Semantic Kernel. You have designed multi-agent workflows with these tools, not just run their tutorials. (Required)
  • ● LLM APIs and model ecosystems: Anthropic Claude API, OpenAI, open-source models (Llama, Mistral). You understand cost, latency and quality trade-offs. (Required)
  • ● Cloud infrastructure: AWS (Bedrock, Lambda, SageMaker, S3). Comfortable deploying and monitoring AI systems in cloud environments. (Required)
  • ● Model evaluation and observability: you design evals, not just run them. Experience with LLM-as-judge, RAGAS, Promptfoo, Langfuse or equivalent. (Required)
  • ● API development: FastAPI or equivalent. You can build a production-ready API around an AI system. (Required)
  • ● DevOps basics: Docker, Git, CI/CD pipelines. Your code ships, not just runs locally. (Required)
  • ● MCP (Model Context Protocol) server design and implementation. (Preferred)