Python LLM Trainer | Machine Learning Expertise | LLM Trainer @AppleAs a Python LLM Trainer at Apple, I develop high-quality code to train large language models (LLMs) for a GPT model, focusing on enhancing natural language understanding and generation capabilities.• Developed and implemented high-quality Python code for training GPT models, focusing on improving performance and accuracy.• Contributed to research and development efforts in NLP, exploring new techniques for model training and optimization.• Designed and implemented data preprocessing pipelines to ensure high-quality training data for GPT models.

A versatile software development company where I've primarily focused on creating innovative mobile apps using Flutter. I've played a key role in the successful publication of two apps on both major app stores, demonstrating my ability to deliver end-to-end mobile solutions. I've also leveraged my Python skills to automate data acquisition through web scraping using Selenium and briefly contributed to backend development with Node.js, showcasing my adaptability and eagerness to tackle diverse challenges.

At Peacedev, I've thrived in a dynamic, full-stack development environment, leveraging a diverse range of technologies to create innovative solutions across various industries:• TMT Global: Developed a cutting-edge online crypto learning platform (Python/Flutter) to empower users with financial knowledge.• BlaBla: Created an interactive online teaching app (Python/Flutter), facilitating seamless scheduling and communication between students and teachers.• Unity: Built a crowdfunding platform (Python/Flutter) dedicated to supporting impactful social projects.• Belihebe: Maintained an online e-commerce platform. Leveraged third-party APIs to analyze metrics like acne, redness, and pores, providing users with personalized insights, featuring innovative facial status analytics (Vue.js/Python).

At Molova.co, I developed and maintain a critical web scraper that aggregates clothing data from multiple brands nationwide. Initially built with Selenium, I transitioned the scraper to a more scalable and efficient Django Rest Framework-based API, leveraging Celery workers for handling long-running processes.