R&D Engineer | Flutter Developer | MLOps Specialist
🎓 Computer Engineering Graduate (Abdullah Gül University)
💼 R&D Engineer @ FreshSens | Cross-platform Flutter Apps + ML-powered Alert Systems
🧠 Passionate about Machine Learning & MLOps | Building AI-powered, production-ready systems
✅ Completed MLOps Bootcamp @ Veri Bilimi Okulu (2025) with a production-grade capstone project
Languages: Python, Dart
ML & Data: TensorFlow, Keras, Scikit-learn, Pandas, NumPy
MLOps & DevOps: Docker, Terraform, MLflow, Joblib, Swagger UI, Uvicorn, CI/CD (Jenkins), Crontab, Linux Shell, Bash
Backend & APIs: FastAPI, SQLModel, Pydantic, RESTful APIs, JWT Authentication
App Development: Flutter, MVVM, MobX, Provider, WorkManager
Databases & Storage: MySQL, PostgreSQL, SQLite, Firebase, AWS S3
Other Tools: Git, GitHub, GitLab, Postman
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🧠 Mental Health Treatment Prediction – MLOps Pipeline
MLflow-tracked Logistic Regression pipeline deployed with Docker and FastAPI. Includes data drift detection and MySQL-backed REST API. -
📉 Bank Customer Churn Predictor
End-to-end pipeline with Scikit-learn’s ColumnTransformer + RandomForestClassifier. Exposed via FastAPI and containerized for deployment. -
🔷 MLOps Advertising API
Dockerized FastAPI service for regression model prediction with Terraform deployment -
🔶 Taxi Trip API
MySQL-backed, JWT-authenticated RESTful API for NYC taxi data -
💧 AquaWise – Top 10 @ACT28 Hackathon
An IoT + AI solution for water conservation, built with image processing and anomaly detection logic -
🧠 Speech Recognition
CNN-based Turkish voice command recognizer using TensorFlow and Librosa
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📍 Published in IEEE Xplore:
“Performance Evaluation of TLS 1.3 Handshake on Resource-Constrained Devices...”
Read Paper → -
🔬 Anomaly Detection Pipeline (Time Series)
PCA + Scikit-learn model, automated with AWS S3, Crontab, and retraining workflows -
📱 Cross-platform FreshSens App
Built using Flutter (MVVM, RESTful API, WorkManager, MobX) for sensor data visualization and analysis
✅ MLOps Bootcamp @Veri Bilimi Okulu (2025)
Hands-on training on:
- End-to-end ML Lifecycle
- CI/CD for ML systems
- Model Deployment, Drift Detection
- FastAPI, Docker, Jenkins, MLflow, Terraform, AWS