Toader Carausus
I am a Senior Software Developer with over 6 years of experience in back-end, full-stack, and cloud-native systems.
About
I am a Senior Software Developer with over 6 years of experience in back-end, full-stack, and cloud-native systems. I specialize in modernizing legacy enterprise applications, building REST APIs, and introducing DevOps and QA practices. My expertise spans Java, Kotlin, Python, C#, SQL/NoSQL databases, AWS, Docker, Redis, CI/CD pipelines, NodeRed, and additional experience in AI/ML using reinforcement learning and MLFlow.
I have successfully led teams and managed QA to mentor secure coding and TDD practices across various technologies including Java, Kotlin, Python, and TypeScript. I have a proven track record in migrating legacy applications, enhancing performance, and delivering robust, scalable software solutions in diverse sectors such as healthcare, research, and leasing platforms.
- 8 years' experience
- Joined September 2025
Industry experience
- Software & Internet
- Healthcare
- Education
- Professional Services
- Financial Services
Work experience· 7 roles
Modernised a 30-year-old leasing platform for one of Germany's largest leasing companies, ensuring regulatory compliance and preventing client fines. Improved API latency dramatically, refactored codebase from Java to Python enhancing response times significantly. Secured REST API with robust session management. Upgraded technology stack including Java 1.8 to 21, Python 3.8 to 3.12, Ubuntu 20 to 24, and PostgreSQL 12 to 17. Developed testing frameworks from scratch boosting defect detection by 35%. Created RMI conversion/testing tools for cross-language interoperability between Java and Python. Provided team leadership and QA mentoring introducing automated CI/CD pipelines and authoring Git workflow and QA guidelines.
Designed reinforcement learning experiments in an open-ended multi-stage game comparing multiple training strategies. Built Python data pipelines to capture children's play sessions and train human-like behavioral models. Conducted MLFlow analysis on 200+ runs evaluating parameters and performance, facilitating lightweight experimental architecture for rapid approach comparison.
Migrated analytics pipelines converting AWS S3 JSON data to Parquet format using Lambda and Glue. Integrated dashboards with AWS QuickSight and dockerized Python Lambdas tested with Pytest and Moto. Enhanced Kotlin prototype with AWS Cognito integration.
Education
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