Theofilos Papapanagiotou
Sr Applied Scientist, ICON at Amazon
Amsterdam, North Holland, Netherlands
Summary
🤩
Rockstar🎓
Top SchoolTheofilos Papapanagiotou is a senior applied scientist with 12 years of experience building production-grade ML and deep learning systems, currently driving research and deployment work at Amazon ICON in Amsterdam. He has a strong track record delivering high-throughput, containerized solutions—most notably leading Skynet, a real-time operations tool processing millions of events per second and iterated thousands of times to improve fulfillment efficiency. His background spans ML architecture, MLOps and data science across e‑commerce and telco/media, with hands-on contributions to KServe to improve serverless model serving on Kubernetes. Theofilos combines academic training in AI and data communications with early-career Unix systems expertise, giving him a rare blend of infrastructure savvy and modeling rigor. He is active in open source and practical data science work, evidenced by repository contributions that range from R-based modeling to platform-level enhancements.
12 years of coding experience
9 years of employment as a software developer
Master of Science, Artificial Intelligence, Master of Science, Artificial Intelligence at UvAMaster of Science, Data Communications, Master of Science, Data Communications at Brunel University LondonBachelor of Science, Computer Technology/Computer Systems Technology, Bachelor of Science, Computer Technology/Computer Systems Technology at Piraeus University of Applied Sciences
Greek, English
Stackoverflow
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47answers
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docker-compose
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docker
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Github Skills (36)
kubernetes10
r10
data-manipulation10
machine-learning10
exploratory-data-analysis10
eda10
tensorflow10
kubernetes-pods10
byte10
knative10
data-analysis10
evaluation9
docker-compose9
python9
docker9
Programming languages (16)
Github contributions (5)
theofpa/datascience
Apr 2014 - Jun 2016
Role in this project:
Data Scientist
Data ScientistContributions:164 commits, 2 pushes in 2 years 3 months
Contributions summary:Theofilos's commits focus on data analysis and model building within a data science context. The code demonstrates the usage of R libraries such as `XML`, `jsonlite`, `data.table`, and `xlsx` for data manipulation, web scraping, and data import. The user explores a dataset, performs exploratory analysis, and builds and evaluates machine learning models using the `caret` package and random forests.
kserve/kserve
May 2020 - Oct 2021
Standardized Serverless ML Inference Platform on Kubernetes
Role in this project:MLOps Engineer
MLOps EngineerContributions:46 reviews, 19 commits, 19 PRs in 1 year 5 months
Contributions summary:Theofilos primarily focused on enhancing the KServe platform's functionality related to model serving. They implemented features such as a `defaultTimeout` for predictors and applied it to the TensorFlow model server, incorporating it into configurations and tests. Furthermore, the user modified code to support Knative, specifically by updating variables and fixing potential issues. Their work involved adapting the system to newer versions of Knative and Istio, reflecting their role in maintaining the platform's compatibility and features related to deployment.
xgboostsklearnserverlessclient-goknative
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