• Conducted through research and evaluation of emerging tools and technologies to identify optimal solutions for enhancing system performance and functionality.• Produced documentation outlining project specifications, architectural designs, and implementation strategies.• Implemented new features and functionalities within the Fliff platform, leveraging best practices to deliver robust and user-friendly solutions.• Undertook the task of refactoring legacy code, applying modern coding standards and optimization techniques to streamline processes and enhance maintainability.• Developed and executed unit and behavior-driven development (BDD) tests to validate the functionality and performance of newly implemented features, ensuring the reliability and stability of the application.• Conducted rigorous code reviews to assess adherence to coding standards, identify areas for improvement, and promote code quality and consistency across the project

• Implemented RESTful API integration to facilitate seamless communication between different components of the application. • Performed optimizations of the code to boost response time. • Successfully integrated third-party services to the app. • Participated in technical negotiations with the bank partners. • Managed and trained the support team of the app, as a result decreased time of response to users. • As part of the team, I conducted code reviews to ensure adherence to best practices, maintain code quality, and identify opportunities for optimization and improvement.

• Implemented RESTful API integration to facilitate seamless communication between different components of the application. • Successfully integrated third-party services into the WaveNeuro Consumer App, enhancing its functionality and user experience. • Designed and built the application's architecture from scratch, ensuring scalability, performance, and maintainability. • Set up a robust CI/CD pipeline to automate the software development workflow, enabling quick and reliable deployment of new features and bug fixes. • Worked extensively with AWS, leveraging services such as AWS Lambdas, AWS Cognito, AWS API Gateway, AWS Aurora RDS, and AWS S3 to create a secure and scalable infrastructure for the application. • Conducted code reviews to ensure adherence to best practices, maintain code quality, and identify opportunities for optimization and improvement.

Built a RESTful API architecture, ensuring seamless interaction between the various components of the FaceNeuroVision platform.Worked with rtsp streams using specialized techniques to extract and process real-time video data for accurate face recognition.Worked with machine learning (ML) to improve the accuracy of facial recognition algorithms implemented in the platform.Developed and integrated new services that enrich the functionality and versatility of the platform.Provided specialized maintenance and support for existing features and functionality, promptly resolving any issues. Conducted thorough optimization of existing algorithms.