Profile

AI Engineer with proven expertise in designing, training, and deploying scalable machine learning models — especially in computer vision. Adept at full-stack AI development from data preparation to API deployment, delivering measurable business impact. Experienced in working as a freelance AI specialist, successfully delivering end-to-end solutions for diverse clients and projects.

Education

Database Programming Branch

Yahya Kaptan Vocational Technical and Anatolian High School
2017 – 2021Kocaeli, Turkiye
Skills
Programming Languages

Python(Advanced), JavaScript(Advanved)

Deployment & DevOps

Docker

Frameworks & Tools

TensorFlow, Keras, Scikit-learn, FastAPI, Git, Pandas, NumPy, React Native

Professional Experience

AI Engineer Intern

Bluesense AI

Developed and deployed computer vision models for, documented deliverables for regulatory bodies, and led end-to-end productization.

07/2025 – 08/2025Remote

Project Management Intern

SCA SOCIAL

Gained training in project management, IT law, AI & Data Science, and organizational skills. Built and presented multiple projects integrating data science and management concepts.

04/2025 – 05/2025

Interface Developer

POYRAZ (TEKNOFEST Autonomous Vehicle Competition)

Developed ReactJS interfaces for the autonomous vehicle team. Strengthened teamwork, problem-solving, and planning skills in a high-pressure competition environment.

11/2023 – 2024Manisa, Türkiye
Projects

Plantly : Plant Care App

Designed and implemented a plant disease detection system using CNNs (accuracy: 95%), integrated an LLM-based recommendation engine for personalized care tips, and built a React Native app with FastAPI backend serving.

04/2025 – 10/2025

MotivAI - AI Fitness Assistant

Co-developed an AI-powered fitness assistant providing personalized training programs and movement form analysis. Led the AI and backend servicing components, integrating diet and workout data for custom recommendations.

04/2025 – Present
ORHAN BURAK TURAN
1 / 2

Gilberbot : Recipe Suggestor

In this project, I compared the performance of the Qwen 3 4B model using both Retrieval-Augmented Generation (RAG) and Fine-Tuning on the FOOD.COM dataset. For the RAG pipeline, I utilized 200K data samples, while the fine-tuned model was trained on 4K carefully selected entries. I exposed both models via a FastAPI service and built a dynamic, modern user interface using Next.js.

Languages
English

Upper-Intermediate (B2)

Certificates
ORHAN BURAK TURAN
2 / 2