Abel Lavieri

Experience

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  Microverse

  Mar 2021 - Mar 2022

  Full-stack Developer Mentor (Volunteer)

  During my time in the Microverse program, I had the opportunity to develop several projects and assist numerous developers worldwide in completing the program while enhancing their code quality through pull requests.One notable project I contributed to was the eSports Gaming Center:Implemented specialized authentication solutions within both React frontend and Ruby on Rails backend repositories, reducing development time by 20% and significantly improving project efficiency.Led the team in the technical decision to adopt a headless architecture, streamlining the development process by preventing integration conflicts between React and Ruby on Rails. This approach resulted in a 20% faster development cycle, enabling the timely delivery of the MVP. Show less

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  Cleteci

  Mar 2022 - Nov 2023

  Full-stack Developer

  Developed and maintained server-side application logic for the TravelContact project using Ruby on Rails, revamping multiple modules and achieving a 15% boost in overall customer satisfaction.Enhanced the response time of the trips module by approximately 30% through optimized database queries and serializers, ensuring improved performance and user responsiveness.Standardized agent commission payments by creating the initial version of the agent reconciliation module, resulting in a 30% improvement in payroll rates and heightened satisfaction among agents.Streamlined the trip creation process by implementing a flight auto-completion feature, and integrating a third-party flight tracking API, which significantly enhanced efficiency and user experience.Elevated the Ruby on Rails code test coverage to 80% (as measured by SonarCloud), leading to a more robust and reliable codebase overall. Show less

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  Outlier

  Aug 2024 - now

  Full-stack Developer

  Review the code quality of LLM responses, identifying critical errors and implementing enhancements to improve overall code quality.Restructure SQL queries generated by the LLM, minimizing unnecessary joins and intermediate tables to ensure the generation of high-quality future queries.Assess and evaluate the quality of prompts provided to the LLMs, measuring adherence to set parameters and enhancing response accuracy and relevance.