Diego Oliveira Bomfim

Diego Oliveira Bomfim  new_releases

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Software Engineer
São Carlos, State of São Paulo, Brazil
Contact Diego regarding: 
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Full-time jobs
Starting at USD2k/month
Flexible work
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Résumé

Jobs verified_user 25% verified
  • CI&T
    Senior Software Engineer - Center of Excellente AWS GenAI public Remote experience
    CI&T
    Sep 2025 - Current (9 months)
    - Build and deploy Generative AI solutions (LLMs, RAG, vector databases, agents) on AWS - Architect scalable, secure GenAI systems, applying AWS best practices (cloud-native design, cost, security, observability) - Design prompts, contexts, and AI workflows for reliable, high-quality outputs - Develop full-stack cloud applications using Python, Angular, Next.js, and modern frameworks - Handle data ingestion and preprocessing for AI pipelines and knowledge bases - Automate delivery with CI/CD pipelines and Infrastructure as Code - Propagate AWS and GenAI best practices across teams within the Center of Excellence - Explore and validate new GenAI tools, AWS services, and industry trends
  • Torre.ai
    Freelancer in Software Engineering and Operations verified_user Verified experience public Remote experience
    Torre.ai
    Jun 2024 - Sep 2025 (1 year 4 months)
    - Develop and maintain proprietary and client software, software testing, write automated tests, and ensure quality deliverables; - Perform recurrent product-related guidance reviews; - Prepare reports and other materials or documentation as requested; - Report bugs and usability issues.
  • K
    Product-Minded Software Engineer public Remote experience
    KnowCode
    Jul 2023 - Sep 2025 (2 years 3 months)
    - Lead and develop KnowCode's New Products team; - Consult in AI technologies and prompt engineering; - Manage project management and development processes; - Develop proprietary and client software.
  • S
    Software Developer public Remote experience
    SANTA CASA HOSPITAL OF ANDRADINA
    Jul 2018 - Aug 2020 (2 years 2 months)
    - Develop, manage, and maintain the transparency and ombudsman portals for the Santa Casa Hospital of Andradina using Web Development (HTML, PHP, and JavaScript).
Education verified_user 0% verified
  • Udemy
    Product Development & Systems Engineering (INCOSE / 15288)
    Udemy
    Jul 2024 - Aug 2024 (2 months)
    This online course focuses on product development, systems engineering, and product management. It covers the entire life cycle of complex products, from concept to completion. Throughout the course, I learned how to define and manage product hierarchies, involve key stakeholders, and gather system requirements. It also explores how to apply the Systems Engineering Method to solve problems, set milestones, and make informed decisions during development. Additionally, this course provided insights into managing technical processes, defining system boundaries, and selecting the best life cycle strategy for projects.
  • University of Sao Paulo
    Bachelor of Science (B.Sc.), Electrical Engineering
    University of Sao Paulo
    Jan 2019 - Dec 2023 (5 years)
  • F
    Computer Technician
    Federal Institute of Education Science and Technology of São Paulo IFSP Campinas Brazil
    Jan 2016 - Dec 2018 (3 years)
    Technical course in IT with a focus on programming, lasting 3 years (2016-2018), where I learned programming from beginner to advanced levels. Algorithms, programming languages (C, C++, C#, Java, Web development), database disciplines in SQL Server and MySQL, object-oriented programming, and networking.
  • Udemy
    PROJECT MANAGEMENT WITH AGILE METHODOLOGY
    Udemy
  • Vanderbilt University
    LARGE LANGUAGE MODELS AND PROMPT ENGINEERING
    Vanderbilt University
  • P
    DEEP LEARNING WITH PYTORCH FOR MEDICAL IMAGE ANALYSIS
    PIERIAN TRAINING
Projects (professional or personal) verified_user 0% verified
  • Universidade de São Paulo
    Artificial Intelligence Model Training for Blood Cell Region Segmentation
    Universidade de São Paulo
    Jan 2022 - Jun 2023 (1 year 6 months)
    This work involved training and implementing an AI model for segmenting and classifying blood cell regions, including basophils, eosinophils, lymphocytes, monocytes, and neutrophils. Using the U-Net architecture, designed for biomedical image segmentation, the neural network achieved 99.34% accuracy and 1.03% loss after 30 training epochs on a dataset of 1145 images. The Sørensen-Dice coefficient reached 97.16%, reflecting high segmentation quality. Data augmentation enhanced the variety of training images. The model delivered highly accurate segmentations, with minimal discrepancies for complex cell types, ensuring reliable and precise results.