Software Engineer with deep expertise in machine learning, MLOps, and Cloud Security. Passionate about building scalable AI systems, mentoring engineers, and catching waves.
A decade of building innovative solutions, from elite intelligence units to leading tech companies
Engineering
Tel Aviv, Israel
Principal Software Engineer
Tel Aviv, Israel
Tech lead of six engineers, ensuring delivery of scalable API security solutions. Architected the new API Security Platform and presented solutions at major tech conferences.
Lead Software Engineer
Tel Aviv, Israel
Scrum Tech Lead for 6 engineers. Designed and developed a Generative AI Assistant Platform (AgentForce) and ML applications for sales forecasting.
Software Engineering Manager
Tel Aviv, Israel
Led 7 software engineers developing an ML Observability platform. Built microservices architecture and presented at major ML conferences.
Software Engineering Manager & Software Engineer
Tel Aviv, Israel
Developed PaaS services for on-prem cloud platform. Designed and developed ML applications in microservices environment and Big Data as a Service over S3.
Sharing knowledge and insights at major tech conferences worldwide
Demonstrated how to use Google Service Extension Beta to inspect HTTP traffic from Google Application Load Balancer, identify attacks, and integrate with Palo Alto Prisma Cloud.
Key Topics:
Showcased how data scientists and ML engineers can leverage ML monitoring to find the best data and retraining strategy to resolve machine learning performance issues.
Key Topics:
Discussed tools and strategies for building scalable ML pipelines that can quickly monitor, segment, retrain, and experiment on data.
Key Topics:
Explored how model performance degrades over time and strategies for data-driven retraining using production insights rather than just recent data.
Key Topics:
Conference Talks
Audience Reached
Technical Articles
Continents
Sharing insights and best practices from real-world engineering challenges
Refreshing a model's training observations isn't always enough to predict future results better. Using observability of model production data gives us a clear path to better decisions on our retraining strategies.
Building a great, scalable, and easy-to-integrate product starts with design. Follow superwise's journey to API-first and what considerations you should make before embarking on that journey.
Testing microservices that work on a massive data scale is complex. I designed an efficient architecture to make testing easier using Jupyter Notebook and Papermill.
Deploying ML for serving in production doesn't have to be complicated. Learn why FastAPI can speed up this process and save significant development time.
Scaling data-driven microservices requires infrastructure that can accommodate resource changes, but more importantly, code architecture that supports scalability.
I regularly publish articles on Medium about ML engineering, MLOps, software architecture, and emerging technologies.
Have a project in mind or want to discuss opportunities? I'd love to hear from you.
When I'm not building software, you'll find me on the water
Available