⭐️ Mozilla Awardee - Open-Source AI for Environmental Justice
🌎 Tech Lead & Founder building data + AI systems for real-world impact
🌱 Currently exploring AI agents, data platforms, and scalable ML systems
💻 Strong believer in open source as infrastructure for public good
📫 How to reach me: fernanda.carles (at) gmail.com | LinkedIn

I’m a Machine Learning Engineer and Data Scientist with a background in mechatronics engineering, working at the intersection of AI, data engineering, and civic technology.

My work focuses on designing end-to-end data systems: from ingestion and modeling to deployment and real-world applications. I’m particularly interested in time series forecasting, environmental data, and production-grade ML systems.

I’ve led and contributed to projects that combine open data, AI, and public health, building tools that help people and institutions make better decisions.

Tech Lead and Founder of Respira, an open-source platform for air quality monitoring and forecasting in Paraguay.

• Built a modular data platform integrating heterogeneous sources (sensors, APIs, weather data)
• Designed data pipelines (Airbyte + dbt + Prefect) and scalable architecture
• Developed ML models for air quality prediction (time series forecasting)
• Focused on accessibility and real-world usability for citizens and public institutions
• Designed to be replicable across Latin America

Respira combines AI + environmental data + open infrastructure to address a critical gap in public health information.

Contributor to AireLibre, an open environmental data initiative.

• Worked with open air quality datasets and APIs
• Contributed to improving data accessibility and interoperability
• Supported the ecosystem of community-driven environmental monitoring

Collaborated with organizations such as Open Knowledge Foundation, Data Privacy Brasil and TEDIC on initiatives related to open knowledge, digital security, and data governance.

• Contributed to projects at the intersection of technology, public interest, and digital rights
• Supported efforts around data protection, responsible data use, and open access to information
• Worked in multidisciplinary environments bridging technical development, policy, and civic impact

• Machine Learning for time series and forecasting
• Data engineering and pipeline orchestration
• AI agents and LLM-based systems
• Production ML and MLOps
• Open-source infrastructure for civic tech