Eugene Fedorenko
Principal Software Engineer at Microsoft
Greater Seattle Area United States
Summary
🤩
Rockstar🎓
Top SchoolEugene Fedorenko is a Principal Software Engineer based in the Greater Seattle Area with nine years of focused experience building cloud-native infrastructure and MLOps solutions. Now at Microsoft, he drives production-grade automation for Azure landing zones and AKS deployments, blending DevOps and cloud engineering expertise to simplify complex deployment workflows. His open-source contributions include improving Azure Monitor exporters in OpenTelemetry and enhancing Azure ML/DevOps pipelines and Databricks integration, showing a strong streak in observability and model operationalization. Previously a senior architect and long-tenured middleware leader, he brings deep systems design and delivery experience spanning R&D to enterprise platforms. Colleagues rely on him to translate nuanced infra requirements into reliable, testable automation—he often surfaces subtle deployment edge cases (like naming collisions and rover integration) before they reach production. Trained as a computer scientist at KhAI, he pairs pragmatic engineering with a penchant for improving developer workflows.
9 years of coding experience
24 years of employment as a software developer
National Airspace University "KhAI", Kharkiv
Github Skills (35)
azure-devops-server10
unit-testing10
github-ci10
azure-application-insights10
python10
bash10
azure-machine-learning10
terraform10
cicd10
azure-aks10
terraformer10
azure-kubernetes-service10
script10
go10
mlops10
Programming languages (17)
Github contributions (5)
microsoft/MLOpsPython
Jul 2019 - Mar 2020
MLOps using Azure ML Services and Azure DevOps
Role in this project:
MLOps Engineer
MLOps EngineerContributions:1 release, 51 commits, 85 PRs in 7 months
Contributions summary:Eugene primarily contributed to the MLOps aspects of the repository, as evidenced by the changes in pipeline definition files, and the addition of a Databricks integration. Their work included creating and modifying Azure ML pipelines for model training and evaluation, enabling unit tests, and incorporating dataset versioning. They also focused on creating unique resource names to avoid naming conflicts.
devopsazure-mlmlopsazure-machine-learningmachine-learning
Azure/caf-terraform-landingzones-accelerator
Mar 2021 - Jun 2021
Starter project for Applications (level 4) Cloud Adoption Framework for Azure landing zones on Terraform
Role in this project:DevOps Engineer & Cloud Engineer
DevOps Engineer & Cloud EngineerContributions:295 commits, 2 PRs, 39 pushes in 2 months
Contributions summary:Eugene's commits primarily revolve around automating the deployment workflow and infrastructure configuration for an Azure Kubernetes Service (AKS) cluster. They focused on modifying deployment scripts, specifically `deploy_level.sh` and `deploy_level_with_rover.sh`, incorporating features like auto-approve and integrating Terraform with the `rover` tool for deployment. Additionally, the user implemented and tested components related to AKS configurations and infrastructure settings. The commits also involved setting up and testing the infrastructure for workloads.
azure-landing-zonesadoptioncloud-adoptioncloud-adoption-frameworkzones
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