At Uto.ai, I led the development of a multilingual, LLM-based chatbot that improved service delivery and user interaction across various countries. I managed a team of five engineers, focusing on complex, cross-functional communications in a fast-paced environment.
Spearheaded the AI team, focusing on talent acquisition, training, and mentorship. Implemented effective strategies for team growth and skill enhancement.
Collaborated with stakeholders to align project goals with business objectives, ensuring cross-functional collaboration for project advancement.
Enhanced chatbot interactions by integrating vector databases with Retrieval-Augmented Generation (RAG), creating a customizable knowledge base, enhancing character interaction and user experience. Enabled support for multiple languages, catering to a diverse user base and showcasing the project's international reach.
Utilized MongoDB to build a long-term memory system for the chatbot, enabling the retention and contextual use of information across interactions.
Led the development of RESTful APIs, ensuring seamless integration and communication between the chatbot and external services.
Conducted code reviews to maintain high standards of code quality, adherence to best practices, and to foster a culture of continuous learning and improvement within the team.
Manage a project of a real-time forecasting system for online games. Analyzed the data using Python libraries such as Pandas, Scikit-Learn, TensorFlow to extract features to analysis user-behavior and built statistical models in Python based on ML algorithms like SVM, Logistic Regression, Neural Networks.
Implementation of Object Detection and Face Recognition on embedding systems. These functions help users to classify photos automatically from the background with only a few hardware resources.
Testing the ability to use TensorFlow to build deep learning models for a range of tasks such as regression, computer vision, natural language processing, and time series forecasting.
Use EfficientNet to detect products in images. Reach the top 30%.
Cleaning data. Flagged out the late deliveries and penalties are imposed on the providers to ensure they perform their utmost.
At Uto.ai, I led the development of a multilingual, LLM-based chatbot that improved service delivery and user interaction across various countries. I managed a team of five engineers, focusing on complex, cross-functional communications in a fast-paced environment.
Spearheaded the AI team, focusing on talent acquisition, training, and mentorship. Implemented effective strategies for team growth and skill enhancement.
Collaborated with stakeholders to align project goals with business objectives, ensuring cross-functional collaboration for project advancement.
Enhanced chatbot interactions by integrating vector databases with Retrieval-Augmented Generation (RAG), creating a customizable knowledge base, enhancing character interaction and user experience. Enabled support for multiple languages, catering to a diverse user base and showcasing the project's international reach.
Utilized MongoDB to build a long-term memory system for the chatbot, enabling the retention and contextual use of information across interactions.
Led the development of RESTful APIs, ensuring seamless integration and communication between the chatbot and external services.
Conducted code reviews to maintain high standards of code quality, adherence to best practices, and to foster a culture of continuous learning and improvement within the team.
Manage a project of a real-time forecasting system for online games. Analyzed the data using Python libraries such as Pandas, Scikit-Learn, TensorFlow to extract features to analysis user-behavior and built statistical models in Python based on ML algorithms like SVM, Logistic Regression, Neural Networks.
Implementation of Object Detection and Face Recognition on embedding systems. These functions help users to classify photos automatically from the background with only a few hardware resources.
Testing the ability to use TensorFlow to build deep learning models for a range of tasks such as regression, computer vision, natural language processing, and time series forecasting.
Use EfficientNet to detect products in images. Reach the top 30%.
Cleaning data. Flagged out the late deliveries and penalties are imposed on the providers to ensure they perform their utmost.