Senior AI Engineer, Madrid
2 days ago
Madrid
Senior AI Engineer Puede obtener más detalles sobre la naturaleza de esta vacante y lo que se espera de los solicitantes leyendo la información a continuación. Senior AI Engineer Country: Spain IT STARTS HERE Santander ( ) is evolving from a global, 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 whats possible. This is more than a strategic shift. Its 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. Technology at Santander: Where innovation drives business Being part of the Technology team at Santander means working at the heart of the Groups transformation. Our purpose is to support the business with innovative solutions that have a real impact on customers, employees and communities. We contribute to business challenges by combining cutting-edge technology - from cloud-native architectures and data streaming to generative artificial intelligence - with agile and collaborative methodologies. Here, every line of code, every operational improvement and every process optimisation has a purpose, and everything we build contributes to a more inclusive, efficient and sustainable future. THE DIFFERENCE YOU MAKE About the Foundation Models Team The Foundation Models team is building the next generation of financial AI. Our models are purpose-built and pre-trained to understand the language, data, and dynamics of banking. Unlike general-purpose models adapted for finance, they are trained from the ground up on proprietary financial data spanning transactions, credit, risk, and compliance, across multiple geographies and regulatory environments. This is a rare opportunity to do foundational research with direct production impact. The models built here will power decision systems, intelligent agents, and customer-facing products at global scale. The team operates at the frontier of AI science, with access to financial datasets and compute resources that do not exist outside of institutions of this size. Role Responsibilities The Senior AI Engineer: Foundation Models combines deep individual research expertise with the ability to set technical direction for a small team of engineers. The role owns the full lifecycle of domain-specific foundation model development for banking, from architecture design and pre-training to fine-tuning, evaluation, and deployment. It sits at the intersection of frontier AI research and the complex, regulated reality of financial systems. The role leads the design and execution of pre-training pipelines over large-scale proprietary banking data, covering transaction records, credit histories, regulatory filings, and market signals, ensuring models learn representations that are both statistically powerful and domain-faithful. Working alongside data, engineering, and product teams, the Senior AI Engineer drives fine-tuning and adaptation strategies that align pre-trained capabilities with specific downstream banking tasks such as credit scoring, fraud detection, risk modeling, and regulatory reporting. A critical part of the role is owning the design, build, and ongoing maintenance of the cloud infrastructure that makes large-scale model training possible: compute clusters, distributed training pipelines, containerized environments, and the orchestration systems that keep them reliable at scale. The Senior AI Engineer is expected to make principled architecture decisions about this infrastructure, not simply consume it. Once the team grows this area will be handled by a dedicated infrastructure team, but initially this will be directly managed by scientists to ensure the design fulfills their requirements. A core part of the role is building rigorous evaluation frameworks for financial AI: benchmarks grounded in real banking outcomes, safety and fairness assessments tailored to regulatory requirements, and monitoring systems that ensure model reliability in production. The Senior AI Engineer contributes to shaping evaluation standards that go beyond standard ML metrics, incorporating domain validity, explainability, and compliance considerations. The role has a strong cross-functional dimension, collaborating with risk, compliance, and business line teams to translate model capabilities into deployable solutions, and serving as an internal reference on foundation model methodology. External scientific engagement through publishing, conference participation, and collaboration with academic partners is encouraged and supported. The role might require visits to our Madrid office. Key Responsibilities • Set technical direction for a small group of engineers working on foundation model development, providing scientific leadership, prioritization, and hands-on guidance on the most complex problems., • Design and lead the pre-training of financial foundation models over large-scale, multi-modal banking datasets, including structured (tabular, time-series) and unstructured (text, regulatory documents) data., • Develop fine-tuning and adaptation strategies using supervised fine-tuning, LoRA, and other parameter-efficient methods, targeting specific banking tasks across credit, fraud, risk, and compliance domains., • Build, maintain, and evolve the cloud infrastructure required for large-scale foundation model training: compute cluster provisioning on AWS, Azure, or equivalent platforms containerized training environments using Docker and job orchestration via Kubernetes, SLURM, or equivalent systems., • Build and maintain evaluation frameworks for banking AI: task-specific benchmarks, fairness and bias assessments, regulatory alignment checks, and out-of-distribution robustness tests., • Define data curation, preprocessing, and tokenization strategies appropriate for financial data, including handling of sensitive, imbalanced, and temporally structured datasets., • Work with risk, compliance, legal, and product teams to ensure models meet regulatory expectations (GDPR, Basel III/IV, local central bank requirements) and are deployable in production banking environments., • Stay current with and critically evaluate frontier research in large language models, multimodal architectures, and efficient training methods, translating relevant advances into the teams roadmap., • Potentially contribute to the external scientific community through publications, conference presentations, and collaborative research partnerships. WHAT YOULL BRING Required Skills Experience • 5 to 10 years of experience in machine learning product development or AI research, with a significant portion spent on large-scale model development or applied research in production environments., • Demonstrated expertise in foundation model pre-training: architecture choices, data pipelines, distributed training, and training stability at scale (transformer-based models, LLMs, or equivalent)., • Hands-on experience with model fine-tuning and adaptation techniques, including full fine-tuning, LoRA, and parameter-efficient methods., • Proven ability to set technical direction and provide scientific leadership to a small team of engineers., • Demonstrable experience designing and operating cloud infrastructure for large-scale ML workloads, including cloud platforms (AWS, Azure, or equivalent), container environments (Docker), and job orchestration systems (Kubernetes, SLURM, or equivalent)., • Strong programming skills in Python and proficiency with deep learning frameworks (PyTorch preferred)., • Ability to communicate technical findings and model behavior clearly to non-technical stakeholders including risk officers, regulators, and senior leadership., • Fluent in English and Spanish. Nice to Have • Experience with multimodal architectures integrating structured tabular data with text or time-series inputs., • Published research track record, with peer-reviewed contributions to top AI/ML venues., • Knowledge of privacy-preserving ML techniques such as federated learning and differential privacy, relevant to cross-border banking data., • Prior experience in a bank, fintech, financial regulator, or financial data provider., • Familiarity with any of the following banking data domains: transaction and payments data, credit and risk data, and regulatory and compliance data., • Familiarity with regulatory AI frameworks such as the EU AI Act, SR 11-7, or equivalent model risk management guidelines. xkdbapo, • Experience working within a distributed, multi-country AI organization with global and local delivery accountability. Inteligencia Artificial, LLM, Python
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