Computer vision, LLM/RAG systems and MLOps. A blood-cell classifier that holds up on another lab's camera (56.96% → 89.05% OOD, no retraining), a streaming pipeline on 10 GB of sensor data, and a RAG assistant deployed publicly.
Open to Machine Learning, Computer Vision and AI Engineer roles, remotely or hybrid / on-site near Sakarya. Available part-time or as an intern alongside my M.Sc. studies.
End-to-end white blood cell classification using DenseNet121 with custom WBCAttention and MedSwish layers, achieving 98.53% in-distribution and 89.05% OOD accuracy via retraining-free inference adaptation, featuring a 5-step Medical Enhanced Filter (MEF) pipeline and an autonomous LLM shortcut-detection agent.
A short introduction and the purpose of this portfolio site.
Retrieval-Augmented Generation system answering questions about personal ML and automation projects — combining AST-extracted code structure with hand-curated documentation in a hybrid knowledge base, featuring similarity-threshold gating and project-filtering metadata routing.
End-to-end big data pipeline for classifying factory operator actions from 10 GB, 200 Hz motion-capture sensor streams — covering out-of-core engineering, feature extraction, supervised/unsupervised learning, and real-time streaming with concept drift detection.
Design and implementation of a desktop RPA suite using Selenium and Tkinter, featuring a local mock ERP test-harness for offline end-to-end testing, dynamic retry engines, and multi-threaded GUI systems used to power collectibles micro-venture sync workflows.
A platform-agnostic, config-driven mobile automation suite built with Python and Appium, designed to automate listing management across Turkish C2C marketplaces using coordinate-based pairing and overlap detection algorithms.
Real projects in ML, backend, and automation with measurable outcomes.