Pablo Ibarra is a Senior Machine Learning Engineer with nine years of experience building production-grade ML systems that improve user experiences. Currently at Mavenoid in Oregon, he designs retrieval and generative AI solutions—fine-tuning LLMs, parsing and encoding PDFs for semantic search, and deploying Dockerized MLOps pipelines on GCP. His background spans fintech and ecommerce risk modeling at Returnly and Affirm, plus consulting projects in document OCR, demand forecasting, and churn analysis using tools from LightGBM and PySpark to transfer-learned CNNs. He combines hands-on model development with end-to-end engineering—vector databases, CI/CD, experiment tracking, and integration testing—to move prototypes into robust services. A former freelance Python trainer, he also brings practical pedagogy to complex topics, making advanced ML accessible to teams. Trained in Mathematical Engineering, he pairs strong quantitative foundations with a pragmatic focus on scalable, interpretable solutions.
9 years of coding experience
5 years of employment as a software developer
Coursera
Mathematical Engineering, Specialized in Economathematics, Mathematical Engineering, Specialized in Economathematics at Complutense University of Madrid
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Pablo Ibarra - Senior Machine Learning Engineer at Mavenoid