Veniamin Viflyantsev
Software Developer at JetBrains
Berlin, Germany
Summary
👤
Senior🎓
Top SchoolVeniamin Viflyantsev is a seasoned software developer with 12+ years building resilient microservices and platform tooling, currently working at JetBrains in Berlin. He specializes in JVM ecosystems—Kotlin, Hibernate, gRPC and Jersey—championing TDD and DDD to deliver production-grade systems and owning features from requirements to deployment. Past roles include leading an in-house accounting system, core grid-balancing logic for energy networks, and a migration from monolith to microservices that moved 50GB of file records. He also bridges software engineering and machine learning, contributing to open-source Kotlin deep learning tooling (adding LiSHT activation and performance optimizations) and building NER and routing models for real-world automation. Pragmatic and product-minded, he focuses on measurable impact and operational ownership while seeking relocation opportunities in Berlin.
12 years of coding experience
11 years of employment as a software developer
Bachelor's degree Computer Science, Bachelor's degree Computer Science at Voronezh State University
English, Russian
Stackoverflow
589reputation
82kreached
3answers
4questions
Github Skills (18)
kotlin10
activation-function10
deep-learning10
tensorflow10
data-structure9
algorithm9
algorithms9
machine-learning9
data-structures9
gpu8
process-builder6
encryption6
windows6
amazon-web-services6
amazon-cognito6
Programming languages (5)
Github contributions (5)
Kotlin/kotlindl
Aug 2021 - Oct 2021
High-level Deep Learning Framework written in Kotlin and inspired by Keras
Role in this project:
ML Engineer
ML EngineerContributions:28 reviews, 6 commits, 10 PRs in 1 month
Contributions summary:Veniamin implemented a new activation function, LiSHT, extending the existing model loading/saving functionality to support it. They optimized the FloatArray::argmax function to linear time complexity, improving performance. Furthermore, the user refactored the model summary API to utilize a dedicated ModelSummary object, enhancing its structure and readability.
gpudeep-learninghigh-levelmachine-learningkotlin
knok16/pact-jvm
Nov 2015 - Aug 2021
JVM version of Pact. Enables consumer driven contract testing, providing a mock service and DSL for the consumer project, and interaction playback and verification for the service provider project.
Contributions:2 PRs, 2 pushes, 1 branch in 5 years 10 months
contractpactplaybackmockjunit
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