IT STARTS HERE Santander () is evolving from a Country: Spain IT STARTS HERE Santander () is evolving from a integral, high-impact brand into a technology-driven organization, and our people are at the heart of this journey. Together, we are driving a customer-centric transformation that values bold thinking, innovation, and the courage to challenge what's possible. This is more than a strategic shift. It's a chance for driven professionals to grow, learn, and make a real difference. Our mission is to contribute to help more people and businesses prosper. We embrace a strong risk culture and all our professionals at all levels are expected to take a proactive and responsible approach toward risk management. Designing, building and deploying production-grade agentic AI systems
- multi-agent orchestration with real memory, planning, tool use and error recovery. Not demos. Systems that work. Developing and fine-tuning small and medium language models (SLMs) for regulated banking use cases, including custom evaluation frameworks and domain-specific benchmarks. Architecting and implementing MCP servers, A2 A protocols, and federated API layers that allow AI agents to operate across the group’s multi-country infrastructure. Prototyping new ideas in two-week sprints: hypothesis, architecture, code, functional demo, one-pager. This is the lab’s operating rhythm
- you need to thrive in it. Collaborating with researchers, data scientists and business stakeholders to translate complex technical concepts into tangible bank value
- Alchemy-style transformations of legacy assets. Keeping the lab at the frontier: monitoring emerging research, evaluating new tools (Harness Engineering, Kiro, Windsurf, Devin), and integrating them into the lab’s workflow when they add real value. Producing clean, tested,



observable code that can be handed off to the AI Science team for production scaling. You own your code end-to-end. What You’ll Bring 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 4-8 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) Experience fine-tuning or adapting language models (SFT, Lo RA, RLHF) for domain-specific tasks, including dataset curation and evaluation design. (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)



Experience building and consuming REST APIs and integrating AI systems with enterprise data sources, cloud services and third-party platforms. (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) Education 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) Languages 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) Hard Skills Python: advanced proficiency. Clean, tested, production-grade code. You know the difference between a script and a system. (Required) Agentic AI frameworks: Lang Graph, Auto Gen, Crew AI, 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, Open AI, open-source models (Llama, Mistral). You understand cost, latency and quality trade-offs. (Required) Cloud infrastructure: AWS (Bedrock, Lambda, Sage Maker, 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: Fast API or equivalent. You can build a production-ready API around an AI system. (Required) Dev Ops basics: Docker, Git, CI/CD pip

📌 Santander Ai Lab (Madrid)
🏢 Santander
📍 Madrid