José Gama

Bachelor’s degree in Computer Science and currently pursuing a Master’s degree in the same field at the Federal University of Campina Grande, with a focus on the investigation and evaluation of Large Language Models (LLMs) for the Portuguese language. I have 4 years of experience in data science and software development, including Python for web scraping (Selenium), Machine Learning, Neural Networks, and Generative AI (Pandas, Scikit-learn, PyTorch, TensorFlow, Transformers), backend development (Flask), as well as databases (PostgreSQL) and containerization (Docker, Kubernetes). I am currently expanding my skills in cloud computing, with a focus on AWS.

AI Engineer at Distributed Systems Lab (IBM + UFCG) (2024-08 – Present)

Developed and evaluated Large Language Model (LLM)-based solutions on benchmark datasets for code evaluation, subjective language processing, and general knowledge reasoning, focusing on performance, robustness, and real-world applicability.

• Developed and evaluated Large Language Model (LLM)-based solutions on benchmark datasets for code evaluation, subjective language processing, and general knowledge reasoning, focusing on performance, robustness, and real-world applicability.
• Built automated evaluation pipelines integrating multiple APIs (GPT, Gemini, Claude, DeepSeek, Granite, Sabiá) to compare model outputs and generate structured performance reports.
• Designed data preprocessing and analysis workflows for large-scale textual datasets to support model experimentation and optimization.
• Implemented reproducible experiments using Docker and Git, enabling consistent benchmarking and automated re-runs across environments.

Software Developer at Software Practices Lab (BillApp + UFCG) (2023-02 – 2024-01)

Built automated data extraction pipelines (web scraping) to collect and structure billing data from 20+ service providers (water, electricity, gas, telecom), enabling operational scaling and expanded regional market reach.

• Built automated data extraction pipelines (web scraping) to collect and structure billing data from 20+ service providers (water, electricity, gas, telecom), enabling operational scaling and expanded regional market reach.
• Created monitoring and validation routines to detect failures and inconsistencies in data collection processes.
• Transformed unstructured web data into structured datasets for analysis and downstream applications.
• Collaborated with cross-functional teams to improve data availability and reliability for business operations.

Machine Learning Engineer at Data Mining Lab (Dell Technologies + UFCG) (2022-03 – 2023-02)

Applied machine learning models for text classification, performing sentiment analysis and intent classification on large-scale customer support chat datasets to support automated chatbot responses.

• Applied machine learning models for text classification, performing sentiment analysis and intent classification on large-scale customer support chat datasets to support automated chatbot responses.
• Built end-to-end machine learning pipelines for data preprocessing, feature engineering, model training, model evaluation, and deployment of classification and clustering models.
• Developed and trained supervised and unsupervised learning models to improve customer service analytics and response automation.
• Processed and analyzed structured and unstructured data using Python, SQL, and machine learning libraries (Scikit-learn, PyTorch, TensorFlow).

Master of Science (MsC) in Computer Science – Federal University of Campina Grande (UFCG) (2024-08 – 2026-03)

Bachelor of Science (BsC) in Computer Science – Federal University of Campina Grande (UFCG) (2019-03 – 2024-08)