Jerónimo Vásquez González is an AI Engineer (Machine Learning / Models) with 1-2 years of experience, currently a student at Universidad Nacional de Colombia studying Ingeniería de sistemas. He is developing predictive, classification, and segmentation models using Python with TensorFlow and scikit-learn, and builds end-to-end data pipelines for data cleaning, feature engineering, and model evaluation. Jerónimo also uses SQL for data extraction and Power BI for analytical dashboards, focusing on practical machine learning applications and digital transformation. He is looking for knowledge sharing, community, friendships, talent, speaking opportunities, and sponsorships, and is open to introductions via email.

Predictive modeling, classification models, segmentation models, data pipelines, machine learning, artificial intelligence, digital transformation, data analytics, feature engineering, data cleaning, model evaluation, Python (TensorFlow, scikit-learn), SQL, Power BI, Java.

Developing predictive, classification, and segmentation models using Python (TensorFlow, scikit-learn). Current work involves building end-to-end data pipelines that encompass data cleaning, feature engineering, and model evaluation. Additionally, utilizes SQL for data extraction and Power BI for creating analytical dashboards. Projects focus on practical applications of machine learning and digital transformation within a systems engineering framework at Universidad Nacional de Colombia.

Computer Systems Engineering student interested in data analysis, quantitative modeling, and backend development. Experienced with Python, SQL, Power BI, and real-time data processing technologies.

Quantitative modeling, statistical arbitrage analysis, financial data evaluation, machine learning model development, AI applications, backend development, Python, data analysis, statistical and quantitative modeling for insurance and financial risk, AWS infrastructure, Terraform, infrastructure-as-code, cloud technologies, knowledge sharing, collaboration opportunities, and continuous learning in data science and artificial

Current projects include quantitative modeling, statistical arbitrage analysis, and financial data evaluation at UNvesting using Python and SQL. Additionally, I contribute to the development of backend solutions at Etherea, working on the construction and integration of microservices for real-time telemetry data processing using technologies such as Kafka, Docker, Python, and C++. Technical repositories like "Aws_cloud" and "web-aws-terraf

Estudiante de ingeniería de sistemas, he desarrollado varios proyectos de agentes con RAG y desarollo web

Agentes Desarrollo

Rag Agentic