Assaf Moldavsky
Senior Staff AI Engineer at Censys
Brookline, Massachusetts, United States
Summary
👤
Senior🎓
Top SchoolAssaf Moldavsky is a Senior Staff AI Engineer with over a decade of experience designing and shipping end-to-end ML and AI systems, currently leading AI efforts at Censys. He has a proven track record turning research prototypes into production-grade solutions across diverse stacks and clouds, from TensorFlow/TFX pipelines and Databricks to Kubernetes, Seldon, and AWS services. Comfortable bridging data science and engineering, he has driven agentic conversational AI, LLM observability, and performance tuning while translating non-technical ideas into measurable, KPI-driven outcomes. His open-source contributions include operational improvements to the widely used Seldon Core MLOps framework, reflecting a practical focus on model deployment and runtime reliability. A former founder and early full-stack engineer, he blends entrepreneurial product instincts with deep systems-level expertise. Based in Brookline, MA, he’s known for autonomous leadership on multi-quarter projects and an appetite for solving knotty infrastructure and ML problems.
10 years of coding experience
20 years of employment as a software developer
AS Computer Science, AS Computer Science at Massachusetts Bay Community CollegeCompletion Degree IT and MCSE courses, Completion Degree IT and MCSE courses at Kamtec InstituteBA Computer Science, BA Computer Science at Boston University
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Github Skills (24)
kubernetes10
docker10
deploying10
dockers10
mlops10
kubernetes-pods10
safari9
authentication9
java9
machine-learning9
javas9
cicd8
aiml7
ml7
ai7
Programming languages (6)
Github contributions (5)
SeldonIO/seldon-core
Jul 2020 - May 2021
An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
Role in this project:
MLOps Engineer
MLOps EngineerContributions:2 reviews, 20 commits, 5 PRs in 10 months
Contributions summary:Assaf focused on improving the S2I (Source-to-Image) build process for Java wrappers, enhancing the build and runtime image creation with Docker. They updated tests for the Java wrapper, refactored the model controller, and updated method names to ensure proper functionality. Furthermore, they made changes to the operator and deployment configurations, including Istio-related settings, and updated the metrics, highlighting a focus on operational aspects of the ML framework.
monitorservingdata-sciencedeploymentmachine-learning
amoldavsky/angular-mocks-async
Feb 2016 - Apr 2018
Contributions:8 releases, 38 commits, 21 PRs in 2 years 1 month
typescriptangularjsangularasync
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