Eitan Sela
Principal GenAI ML Specialist Solutions Architect at Amazon Web Services (AWS)
Tel Aviv-Yafo, Tel Aviv District, Israel
Summary
👤
Senior🎓
Top SchoolEitan Sela is a Principal GenAI/ML Specialist Solutions Architect with 20+ years of engineering experience and a decade focused on cloud AI, currently helping Israeli enterprises turn Generative AI and ML ideas into production-grade systems at AWS. He blends deep hands-on expertise in distributed systems, MLOps, LLM applications, RAG and inference optimization with strategic technical advising for C-level stakeholders, having shaped adoption at both Microsoft and AWS. Eitan has authored widely used workshops, blog posts, and open-source SageMaker examples—contributing practical code fixes that keep notebooks up-to-date with SageMaker SDK v2—bridging the gap between prototypes and maintainable production. Known for running large-scale enablement programs and speaking at industry events, he also brings a pragmatic engineering background from roles building cloud-native microservices and big-data pipelines. Outside work he’s a 3× marathon finisher, a discipline he likens to tackling long-running technical challenges.
10 years of coding experience
21 years of employment as a software developer
B.SC, Computer Science & Mathematics, B.SC, Computer Science & Mathematics at Bar-Ilan University
Stackoverflow
61reputation
3kreached
2answers
1question
Github Skills (16)
amazon-sagemaker10
machine-learning10
jupyter-notebook10
tensorflow10
aws10
python10
data-science9
scikit-learn9
scikit9
deeplearning-ai9
deep-learning9
xgboost9
pytorch9
jms6
amazon-web-services6
Programming languages (6)
Github contributions (5)
aws/amazon-sagemaker-examples
Nov 2020 - Apr 2022
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Role in this project:
ML Engineer
ML EngineerContributions:53 reviews, 49 commits, 51 PRs in 1 year 5 months
Contributions summary:Eitan made several contributions focused on improving and updating Jupyter notebooks within the `aws/amazon-sagemaker-examples` repository. Their work involved fixing prediction outputs in a TensorFlow script and updating existing notebooks to be compatible with SageMaker SDK v2, which included adapting code for various algorithms and functionalities like XGBoost, Scikit-learn, and PyTorch. Furthermore, the user addressed issues with the documentation and code examples, ensuring the examples' accuracy and relevance.
pythonjupyter-notebooktrainingawssagemaker
aws-samples/amazon-sagemaker-mlops-workshop
Mar 2021 - Nov 2022
MLOps workshop with Amazon SageMaker
Contributions:1 review, 80 commits, 5 PRs in 1 year 8 months
sagemakeramazon-sagemakerworkshopamazonmachine-learning
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