Servando Pizarro Martínez

I work with Azure AI Foundry, deploying and managing agents and prompt flows to automate enterprise workflows. I build production data pipelines from ingestion (web scrapers, connectors) through transformation using Medallion architecture and Data Vault principles for historization, all powered by PySpark and Delta Lake tables in Microsoft Fabric. I develop automated evaluation pipelines and judge-AIs for evaluating model performance, conduct performance benchmarking and optimization of AI models in production, and build advanced reporting and data models to bridge analytics and engineering capabilities.

During my three-month on-site assignment in Lima, I supported international cooperation projects by working with local teams on data initiatives. I built POCs and pilots with local partners, including agentic AI systems that can autonomously access various information flows. I contributed to reporting, process automation, and intercultural knowledge exchange, gaining valuable exposure to development cooperation in practice.

In my role as a Junior Data Specialist, I implemented and programmed in Azure Synapse while innovating with machine learning to improve data efficiency. I brought together the worlds of Data Science and Data Engineering, refining our approach to data and ensuring it was not only managed but also intelligently transformed into valuable insights. By integrating machine learning with scalable Data Vault techniques, I advanced our systems toward smart automation and predictive capabilities, making our data strategies both time-efficient and future-ready.

My dual studies at GIZ combined academic learning with substantial professional experience across multiple departments. I applied university principles in real-world projects, contributed to the workers' council, and developed a strong foundation in digital operations and international project management, gaining hands-on experience in development cooperation.