I am an AI Engineer and Senior Data Scientist with more than 8 years of experience delivering production-grade machine learning systems in the banking sector. My background is centered on credit risk modeling, AML detection, recommendation systems, and MLOps, with a strong focus on building solutions that scale reliably in real-world environments.
I have worked extensively with AWS services such as SageMaker, S3, Athena, EC2, CloudWatch, Lambda, Glue, and RDS, and I have hands-on experience deploying models, managing data pipelines, and monitoring production systems. I also build RAG pipelines, LLM-powered APIs, and end-to-end MLOps platforms using tools like MLflow, DVC, Docker, Kubernetes, Jenkins, and CI/CD workflows.
In my recent roles, I have contributed to government-scale data platforms, taught machine learning to students, and developed scoring and propensity models for major financial institutions. My work has supported compliance, audit, customer targeting, and operational planning, while consistently achieving strong model performance in production.
I am currently completing a Master's degree in Artificial Intelligence while working full time, which reflects my ability to learn continuously and perform under pressure. I also hold a Bachelor's degree in Systems Engineering and a specialization in Financial Institutions Management, which complement my technical and business-oriented approach.
Beyond industry work, I have built several practical projects such as a Financial RAG Assistant, a churn prediction platform, and a credit risk MLOps platform. These projects demonstrate my ability to design full machine learning solutions from data preparation to deployment and monitoring.
I am fluent in Spanish and English, and I am seeking senior ML/AI opportunities in Europe, Canada, or remote settings. I bring a combination of technical depth, banking domain expertise, and a strong record of delivering impactful machine learning systems.
Designed AI system architectures for government-scale data platforms; built and deployed machine learning pipelines processing public labor market data; implemented DevSecOps practices including security scanning, secrets management, and infrastructure as code; integrated microservices using Docker and CI/CD pipelines.
Designed end-to-end ML curriculum for 100+ students per cohort covering EDA, feature engineering, training, validation, and deployment; taught regression, classification, clustering, forecasting, and model interpretability; created hands-on Jupyter notebooks as reference material.
Built propensity and scoring models for financial products with strong Gini performance; developed recommendation models for cross-sell and upsell; trained models on AWS SageMaker and deployed them to real-time inference endpoints; built feature engineering pipelines using AWS Athena and S3; monitored endpoint performance and data drift using CloudWatch.
Built AML risk scoring models for regulatory compliance; developed customer risk segmentation models; led MLOps implementation including model versioning, automated retraining, and monitoring pipelines; developed credit risk models for retail and SME portfolios; used AWS Athena and SageMaker for feature engineering, training, deployment, versioning, and A/B testing; analyzed customer complaint data, built forecasting models, and applied NLP to classify customer feedback.
Master's Degree in Artificial Intelligence
Bachelor's Degree in Systems Engineering
Specialization in Financial Institutions Management
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