Xenia Kramarenko
I’m Xenia Kramarenko, a backend and AI engineer focused on taking machine learning from data preparation all the way to reliable production inference.
About
I’m Xenia Kramarenko, a backend and AI engineer focused on taking machine learning from data preparation all the way to reliable production inference. I’ve built and optimized ML pipelines, ETL workflows, and asynchronous event-driven systems, while also improving performance and reducing infrastructure costs for production AI services.
Across my career, I’ve worked on distributed systems and production operations—designing high-performance web interfaces, integrating authentication and authorization across services, and contributing to real-time platforms. I enjoy bridging engineering and data, ensuring observability, monitoring, and deployment best practices are in place so systems perform well in real environments.
- 11 years' experience
- Joined October 2026
Skills· 16
Languages
- EnglishAdvanced
- Hebrew (modern)Advanced
- RussianAdvanced
Industry experience
- Software & Internet
- Computers & Electronics
- Professional Services
More experts in Dubai, United Arab Emirates
Work experience· 6 roles
Delivered machine learning models to production, including data preprocessing, training, benchmarking, and inference pipelines. Improved performance and optimized infrastructure costs for production AI services. Built ETL pipelines, observability integrations, and asynchronous event-driven workflows. Applied model optimization techniques such as compilation, warm-up strategies, and inference caching.
Led Keycloak integration to provide centralized authentication and authorization across services. Designed and developed a high-performance interface for managing large user datasets with dynamic filtering and real-time updates. Contributed to a real-time bidding platform for campaign budget allocation and management.
Delivered machine learning models to production, including data preprocessing, training, benchmarking, and inference pipelines. Improved performance and optimized infrastructure costs for production AI services. Built ETL pipelines, observability integrations, and asynchronous event-driven workflows. Applied model optimization techniques such as compilation, warm-up strategies, and inference caching.
Education
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