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# AI Engineer

- **Company:** D Local Compliance Santander
- **Location:** Madrid, Spain
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Computer Programming, Data Transformation, Distributed Computing Environment, Monitoring of Systems, Python (Programming Language), Machine Learning, Tokenization, Pytorch, Large Language Models, Deep Learning, Kubernetes, Slurm, Data Pipelines, Docker
- **Published:** August 12, 2026
- **Apply:** https://es.trabajo.org/oferta-5001-81729b3a0376cff71bb5df0a2e58d413

## About the Role

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 team's roadmap. - Potentially contribute to the external scientific community through publications, conference presentations, and collaborative research partnerships. What You'll 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. - Experience working within a distributed, multi-country AI organization with global and local delivery accountability. Your contribution matters , and it's recognized. You can expect a fair, competitive reward package that

## Description

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


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