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Sharing hands-on stories from the AI frontier | Simplifying complexity visually
I’ve spent over a decade helping organizations make AI real for business — not just proofs of concept, but production systems that deliver measurable value. Today, I lead IBM’s AI Client Engineering team across the Americas, where we design and deploy agentic AI solutions powered by watsonx and multi-agent orchestration to solve challenges in IT, HR, finance, and beyond. My technical foundation comes from a Ph.D. in Computational and Applied Mathematics from Rice University, where I built machine learning and optimization models for image processing and dimensionality reduction. On LinkedIn, I share hands-on stories from the field: lessons learned, behind-the-scenes demos, and practical ways teams are adopting agentic AI responsibly and at scale. 🚀 Follow me if you’re curious about what happens when AI moves from the lab to the enterprise — and how to make it succeed: www.linkedin.com/comm/mynetwork/discovery-see-all?usecase=PEOPLE_FOLLOWS&followMember=jorgecasta
Rice University
Doctor of Philosophy (Ph.D.), Computational and Applied Mathematics
January 1, 2009 – January 1, 2014
Rice University
Master of Arts (M.A.), Computational and Applied Mathematics
January 1, 2009 – January 1, 2012
Instituto Tecnológico Autónomo de México
Bachelor of Applied Science (B.A.Sc.), Applied Mathematics
January 1, 2003 – January 1, 2007
IBM
Principal AI Engineer - Gen AI Emerging Technologies
July 1, 2024 – Present
IBM
Principal AI Engineer
July 1, 2022 – September 1, 2024
IBM
Principal Data Scientist & Manager
December 1, 2021 – July 1, 2022
IBM
Sr Data Scientist and Manager @ Data Science Elite Team
April 1, 2020 – December 1, 2021
Towards Data Science
Contributing Writer
January 1, 2018 – Present
IBM
Sr Data Scientist @ IBM Machine Learning Hub
March 1, 2017 – March 1, 2020
IBM
Advisory Data Scientist @ Watson Data Platform
August 1, 2016 – January 1, 2017
IBM
Staff Data Scientist
December 1, 2014 – July 1, 2016
Machine Learning Talks
Invited Talks and Presentations
December 1, 2014 – January 1, 2015
Speaker
Argonne National Laboratory
Givens Associate
May 1, 2010 – August 1, 2010
Greater Chicago Area
Rice University
Ph.D. in Computational and Applied Mathematics
August 1, 2009 – November 1, 2014
Greater Houston
Santander Bank
Quatitative Scientist for Trading on Hedge Funds
January 1, 2008 – July 1, 2009
Mexico City, Mexico
IBM Technology Excellence
IBM
June 24, 2026 – Present
Distinguished Data Scientist
The Open Group
June 24, 2026 – Present
Data Science Profession Certification - Level 3 Thought Leader
IBM
June 24, 2026 – Present
Deep Learning Specialization
Coursera
June 24, 2026 – Present
Sequence Models
Coursera
June 24, 2026 – Present
Convolutional Neural Networks
Coursera
June 24, 2026 – Present
Structuring Machine Learning Projects
Coursera
June 24, 2026 – Present
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
Coursera
June 24, 2026 – Present
Neural Networks and Deep Learning
Coursera
June 24, 2026 – Present
IBM Watson Data Platform Foundations
IBM
June 24, 2026 – Present
Tensorflow
MLconf
June 24, 2026 – Present
Big Data - Spark Fundamentals
IBM
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's long tenure at IBM, progressing through various senior data science and AI engineering roles, demonstrates a strong alignment with large enterprise environments. Their involvement in sharing expertise across the Americas and engaging with product teams for roadmap design indicates a proactive and collaborative approach. The diverse project experience across multiple industries and continuous learning through certifications and writing suggest a strong cultural fit for innovation and continuous improvement.
Soft Skills & Operational Fit
The candidate's extensive experience in client-facing roles, leading teams, and presenting at conferences indicates strong communication, leadership, and problem-solving skills. Their role as a contributing writer further highlights their ability to articulate complex ideas clearly. The focus on agile customer projects and enabling technical communities suggests a collaborative and adaptable operational fit.