Conversational AI Agent for Business Data Analytics at Linen.Cloud

Designed and developed a custom customer-facing conversational AI agent for Linen.Cloud to query structured business data—such as sales rep performance and account assignments—using natural language. Built using LangChain to orchestrate interactions and OpenAI’s large language models, enabling non-technical users to extract insights from tabular data without writing code or using traditional analytics tools.

Machine Learning Engineer Intern at NeoBa AI

Worked in a two-person team to design and develop the minimum viable product (MVP) for ChatGPT for the enterprise. Implemented Python libraries for extracting and aggregating text, tables, and links from websites accessible to OpenAI. Built data extraction capabilities to support enterprise AI workflows.

Machine Learning Engineer Intern at Linen.Cloud

Sales Territory Optimization: Utilized Python for importing and cleaning raw sales territory data, ensuring accuracy and reliability for further processing. Used NumPy for data analysis, identifying critical fields to streamline sales territory allocation. Implemented data normalization and dimensionality reduction techniques to refine the dataset, enhancing the effectiveness of subsequent optimization processes. Developed and tested sales territory optimization solutions using genetic algorithms, specifically SCIP (Solving Constraint Integer Programs). Explored Generative AI for data analysis (GPT-3.5) to summarize and analyze complex data.