Johann Alvares

• Developed, tested, and debugged code for AI and software development projects, ensuring optimal functionality and efficiency.
• Delivered high-quality solutions for leading clients in the AI and LLM industries, enhancing client satisfaction through tailored implementations.
• Conducted in-depth quality assurance and testing, improving process efficiency and project completion rates.
• Evaluated and rated text data to enhance the performance of AI language models, contributing to continuous model improvement.
• Collaborated with cross-functional teams to identify process bottlenecks, implementing solutions that streamlined workflows.
• Played a critical role in refining AI training workflows by ensuring data accuracy and maintaining high-quality outputs.
• Provided insights for the enhancement of AI systems through meticulous analysis and feedback on outputs.

- Improved classification task accuracy by refining machine learning models using Jupyter Notebook.
- Assessed model performance with F1 scores and accuracy metrics, delivering comprehensive analysis reports.
- Developed intent recognition systems and backend tools, streamlining data management with structured knowledge bases.
- Advanced practical machine learning research, particularly in applying LangChain for innovative solutions.
- Specialized in extracting entities from text, with a focus on address extraction.
- Built a sentiment analysis model leveraging techniques like Roberta, Distill Roberta, Random Forest, and Logistic Regression.
- Played a key role in building classifiers and backend systems for a Conversational AI project.

• Improved software reliability by writing unit test cases for APIs, frontend, and backend code, ensuring thorough testing and reducing bugs.
• Documented APIs using Swagger, simplifying integration processes and improving team productivity.
• Enhanced product quality by collaborating with cross-functional teams to design and deploy robust software solutions.
• Identified and resolved software bugs and issues, contributing to smoother product functionality.
• Increased team efficiency by teaching fellow developers effective techniques for writing unit test cases.
• Conducted research and presented insights to support data-driven decision-making in product development.