About Me
I'm a passionate software engineer candidate who thrives on exploring new technologies and implementing them in innovative ways. My dedication to continuous learning and sharing knowledge sets me apart. I enjoy coding, working with data, and leveraging cutting-edge technology to solve problems. As a team player with a supportive and empathetic nature, I excel in collaborative environments.
I've had hands-on experience with various technologies during college, including C# (built a Windows Forms CRUD App with MSSQL), Python, JavaScript-TypeScript, React, ArcJet, FastAPI, Streamlit, TensorFlow, Postman, .NET, Node.js (Express.js), Docker, OpenAI API, Gemini API, Git, GitHub, LangChain, Google Colab, Jupyter Notebook, Anaconda, UIPath, BeautifulSoup, Jira, Trello, Linear, and Slack.
Skills
Work Experience
- Developed a Next.js application that integrated Google Generative AI (Gemini) to analyze images and generate Turkish text descriptions, keywords, and related questions.
- Engineered robust, responsive user interfaces using React, TypeScript, and Tailwind CSS, enhancing the overall user experience with smooth scroll effects and custom error (404) pages.
- Implemented key modules including image upload (with base64 conversion and real-time preview), dynamic keyword extraction, and interactive question generation.
- Utilized Next.js Server Actions with manual RegExp-based validation to ensure secure and efficient server-side operations.
- Strengthened expertise in TypeScript fundamentals—leveraging interfaces, generics, union types, and etc.—to produce reliable, maintainable code.
- Contributed to the deployment process by applying CI/CD best practices and gaining hands-on experience with Docker.
- Collaborated in a fast-paced R&D environment at EMELTEK, acquiring practical skills in prompt design and AI-driven content regeneration.
- Used AI tools to generate and refactor the code (Copilot Workspaces, Copilot in VScode).
Technologies used and acknowledged include Python, Convolutional Neural Networks (CNN), Deep Learning, MLflow, Data Version Control (DVC), Pandas, Langchain, Retrieval Augmented Generation(RAG), and Generative AI.
Projects
- Developed a full-stack LLM-based image analysis application using Next.js, React, and TypeScript integrated with Google Generative AI (Gemini model) for automated image interpretation.
- Engineered a seamless image upload module that converts images to base64 for real-time preview and AI processing.
- Implemented dynamic keyword extraction and question generation features that convert AI-generated descriptions into actionable insights.
- Leveraged Next.js Server Actions with manual RegExp validation to enhance secure, server-side form processing.
- Designed a responsive, modern UI with Tailwind CSS, including smooth scroll effects, custom 404 pages, and toast notifications.
- Applied CI/CD best practices and Docker for efficient deployment and scalable application performance.
- Designed and implemented a CNN architecture using Python and TensorFlow/Keras.
- Preprocessed and augmented data to improve model accuracy and robustness.
- Conducted exploratory data analysis (EDA) to identify key features and patterns in the dataset.
- Prepared and fine-tuned a base CNN model leveraging ImageNet pre-trained weights.
- Optimized the model's hyperparameters to achieve higher accuracy and lower loss.
- Visualized model performance and results using Matplotlib and Seaborn.
- Documented the entire workflow in a Jupyter notebook for reproducibility and transparency.