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Machine Learning Engineer

I am a Computer & Software Engineer specializing in Machine Learning, Deep Learning, Computer Vision, and Large Language Models, with hands-on experience building scalable, production-ready AI systems end-to-end. I have worked across space technologies, mobile development, and microservice-based AI platforms, delivering solutions that combine advanced model architectures with modern engineering best practices.

$ contact --email in/LinkedIn git/GitHub kgl/Kaggle

# Skills

[ Machine Learning ] [ Deep Learning ] [ LLM ] [ RAG ] [ Computer Vision ] [ Image Processing ] [ Python ] [ PyTorch ] [ TensorFlow ] [ LangChain ] [ OpenCV ]

# Experience

// 02/2025 - 06/2025 | Konya, Turkey

Software Engineering Intern (Internship)

@ Entegre Yazılım

Entegre Yazılım is a software company delivering scalable enterprise and mobile solutions across healthcare, logistics, and automation. The company focuses on modern architectures, cloud-ready systems, and high-impact digital products.

Developed a fully functional medication tracking mobile app using React Native & Expo, designed for elderly-friendly usability and offline reliability.
Integrated Google Vision OCR for automatic prescription label scanning.
Built an AI-powered scheduling module using DeepSeek LLM for adaptive reminder generation.
Implemented local notification pipelines, modular state management, and prepared the application for multi-user and wearable device integration.

// 07/2024 - 09/2024 | Ankara, Turkey

Computer Vision Intern (Internship)

@ TUBITAK SPACE Technologies Research Institute

TUBITAK SPACE is a leading government-backed research institution focused on satellite technologies, astronomical imaging, remote sensing, and advanced scientific computing. The institute develops mission-critical systems for national space programs and collaborates with global research partners.

Led the development of an astronomical object detection pipeline specialized in Point Spread Function (PSF) analysis for enhancing star and galaxy identification in FITS images.
Built a custom Faster R-CNN model with PSF-aware layers and integrated Hough-based feature extraction.
Improved detection precision significantly by leveraging distributed training (PyTorch DDP).
Implemented SEP and Photutils tools for source extraction and used Weights & Biases for advanced experiment tracking and metric visualization.

// 07/2023 - 08/2023 | Mersin, Turkey

Software Engineering Intern (Internship)

@ Kardelen Yazılım

Kardelen Yazılım is a technology firm providing enterprise software solutions based on .NET, SQL Server, and cloud architectures. The company supports digital transformation through modular, maintainable, and data-driven applications.

Contributed to the development of enterprise application modules using C# .NET, Entity Framework, and MSSQL Server.
Enhanced database operations, optimized query performance, and collaborated with senior engineers to improve backend reliability.
Supported UI and business logic components, ensuring consistency across the software ecosystem.

# Projects

CreditCard-clustering →

Developed a full unsupervised learning pipeline for credit-card customer segmentation. Includes preprocessing (imputation, winsorization, scaling), engineered behavioral features, dimensionality reduction (PCA, UMAP), and clustering with K-Means, GMM and HDBSCAN. Integrated model evaluation (silhouette, DB index, ARI) and built a Streamlit dashboard for interactive exploration.

Unsupervised Learning Customer Segmentation PCA UMAP K-Means GMM HDBSCAN Streamlit

IRIS-classification-pytorch-mlflow-MLOps →

Built a production-style PyTorch training framework with MLflow experiment tracking and Model Registry. Added a FastAPI inference service with Prometheus metrics, Dockerized workflows, Makefile automation and dual frontends (Streamlit + Gradio). Demonstrates a complete MLOps-ready lifecycle from training to deployment.

PyTorch MLflow MLOps FastAPI Docker Streamlit Gradio Prometheus

Boston-House-prediction-XGBOOST-MLFLOW →

Implemented a robust regression pipeline using XGBoost with comprehensive MLflow tracking. Features preprocessing, domain-specific feature engineering, hyperparameter tuning, model explainability, versioned model management and deployable artifacts suitable for real-world price-prediction systems.

XGBoost MLflow Regression Feature Engineering Hyperparameter Tuning

Gemini-Langgraph (RAG System) →

Developed a full-stack Retrieval-Augmented Generation system using LangGraph, Google Gemini and ChromaDB. Includes a modular document ingestion pipeline, embedding + retrieval workflow, streaming-enabled FastAPI backend, a React/Vite frontend, and Docker-Compose orchestration. Demonstrates modern LLM engineering and production-grade RAG design.

RAG LangGraph Google Gemini ChromaDB FastAPI React Vite Docker

UCI-heart-disease-predictor (MLP / XGBoost / Logistic Regression) →

Created a multi-model classification pipeline for clinical heart-disease prediction. Covers preprocessing, feature engineering, stratified cross-validation, hyperparameter tuning and probability calibration. Combines neural networks (MLP) and tree-based models (XGBoost) to analyze structured medical data.

Classification MLP XGBoost Logistic Regression Medical Data Analysis

crypto-forecaster-LSTM-GRU-Transformer →

Built a high-frequency time-series forecasting engine for crypto markets using LSTM, GRU and Transformer architectures. Includes automated market data ingestion, technical-indicator generation, volatility analysis, S2S modeling, hyperparameter optimization and full backtesting simulations tailored for financial prediction tasks.

Time Series Forecasting LSTM GRU Transformer Crypto Markets Financial Prediction

EuroSAT-Classification-Swin-ConvNeXt →

Designed an end-to-end satellite image classifier using state-of-the-art architectures (Swin Transformer, ConvNeXt). Features mixed precision, warm-up cosine LR scheduling, weighted sampling/Focal Loss for imbalance, patch-based inference, TTA, and a deployable Gradio UI. Demonstrates advanced CV techniques on remote sensing data.

Satellite Image Classification Swin Transformer ConvNeXt Computer Vision Remote Sensing

Astro-psf-detection-resnet →

Engineered a deep-learning pipeline for astronomical PSF detection using a customized ResNet backbone. Workflow includes simulation-driven augmentation (blur, atmospheric distortion), domain-adapted preprocessing, fine-tuning, and morphological post-processing. Shows capability in bridging astrophysics and convolutional networks.

Astronomical PSF Detection ResNet Deep Learning Astrophysics Convolutional Networks

mkyz (Python ML Library) →

Created a lightweight, pip-installable library that simplifies ML workflows: preprocessing utilities, classification/regression/clustering models, automated evaluation metrics, and integrated plotting. Designed for rapid prototyping and educational or experimental ML tasks.

Python Library Machine Learning Preprocessing Classification Regression Clustering

# Education

// 09/2019 - 06/2025

Bachelor’s in Computer Engineering & Software Engineering

Toros University

Completed coursework in Machine Learning, Deep Learning, Data Structures, Operating Systems, and Database Systems. Actively worked on AI-focused research projects including computer vision, time-series forecasting, LLM-based systems, and mobile application development. Participated in university-level engineering competitions and contributed to multiple open-source machine learning projects.