Leonardo Freccero
Software Development Engineer II at Amazon Web Services (AWS)
Wiesbaden, Hesse, Germany
Summary
🤩
Rockstar🎓
Top SchoolLeonardo Freccero is a Software Development Engineer II at AWS with 11 years of engineering experience blending production-grade cloud services, ML research, and embedded systems. He progressed from R&D and SDE internships (Ericsson, Amazon) to roles across DevOps Guru, code transformation, and actions tooling at AWS, pairing strong backend skills with a masters-level background in embedded systems and data science. An active contributor to the widely used GluonTS time-series library, he’s improved WaveNet implementations, performance tuning, and dataset tooling that bridge research and production. Comfortable leading projects and multicultural teams, he brings an unusual mix of marine-science curiosity—longstanding aquarium technician and guide experience—and a drive to combine oceanography with AI-driven solutions.
11 years of coding experience
6 years of employment as a software developer
Bachelor's degree, Computer Science, Bachelor's degree, Computer Science at KTH Royal Institute of TechnologyEIT Digital AlumniMaster's degree, Embedded Systems and Innovation, Master's degree, Embedded Systems and Innovation at Technische Universität BerlinGymnasial naturvetenskaplig spetsutbildning, Marinbiologi, Gymnasial naturvetenskaplig spetsutbildning, Marinbiologi at Gullmarsgymnasiet Lysekil
Swedish, English, Spanish, German
Github Skills (18)
python10
mxnet10
machine-learning10
time-series10
forecasting10
deeplearning-ai10
deep-learning10
forecast10
data-science9
artificial-neural-networks9
pytorch9
dockers9
docker9
neural-network9
pytest8
Programming languages (4)
Github contributions (5)
awslabs/gluonts
Apr 2020 - May 2021
Probabilistic time series modeling in Python
Role in this project:
ML Engineer / Data Scientist
ML Engineer / Data ScientistContributions:34 reviews, 18 commits, 30 PRs in 1 year
Contributions summary:Leonardo contributed to improving the WaveNet model, including fixing timeout issues, optimizing hyperparameters for speed, and adjusting accuracy thresholds. They also implemented the storage of evaluation metrics within the output folder of the training process. Furthermore, the user was involved in moving Dockerfiles and integrating new features, such as RForecastPredictor, within the GluonTS framework. Finally, the user was involved in the implementation of rolling dataset generation and truncation of dynamic features.
forecastingpythontime-series-analysistimeseries-forecastingaws
arangatang/aws-toolkit-jetbrains
Dec 2023 - Jun 2024
AWS Toolkit for JetBrains - a plugin for interacting with AWS from JetBrains IDEs
Contributions:60 pushes, 22 branches in 6 months
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