sinem@dev:~$ whoami
> ML Engineer, building end-to-end machine learning & LLM systems

sinem@dev:~$ cat about.txt
> Experienced and interested in end-to-end machine learning systems,
> data processing pipelines, and model deployment (FastAPI, Docker).
> Actively involved in the development and deployment of modern AI
> systems using LLM-based applications, RAG architectures, and
> vector databases.

sinem@dev:~$ cat contact.txt
> 📧  [email protected]
> 🔗  linkedin.com/in/sinem-gencer
> 💻  github.com/myr-data
> 📍  Turkey, İstanbul

sinem@dev:~$ echo $STATUS
> Tricking rocks into thinking

sinem@dev:~$ _

I work across the full ML lifecycle, from data pipelines and model training to deployment. I currently focus on production grade ML models, LLM-based applications, RAG architectures, and vector databases, alongside classic ML workflows (preprocessing, model evaluation, hyperparameter tuning, interpretability). My background as a Python Instructor teaching applied data science and machine learning, as well as a freelance software developer providing my clients with front-end, back-end, ML model solutions both have improved my social and leadership skills considerably.

Programming: Python, SQL (PostgreSQL, SQLite)

Machine Learning & Data Science: Pandas, Polars, NumPy, Scikit-learn, TensorFlow, Keras, PyTorch, Optuna, LightGBM, XGBoost

NLP & LLM Systems: NLP, LLM, RAG, Embedding, Semantic Search, Prompt Engineering

Data Engineering: Pipelines, ETL/ELT, Preprocessing, Standardization

MLOps & Backend: FastAPI, REST API, Docker, Compose, Kubernetes, MLflow, GitHub Actions

Databases & Search Systems: PostgreSQL, SQLite, Qdrant

Visualization: Matplotlib, Seaborn

Tools & Environments: Jupyter Notebook, VS Code, Google Colab, Git, GitHub

Team-lead on an end-to-end AI system that analyzes incentive and grant programs for SMEs and generates automated recommendations.

• Data pipeline covering scraping, processing, and standardization
• RAG-based chatbot with a Qdrant vector database and embedding-based semantic search
• LLM-based intent classification and query routing system
• Document analysis and information extraction with NLP
• Backend REST API services with FastAPI, containerized with Docker & Compose (Kubernetes-ready)
• KVKK & GDPR–compliant data handling and API security
• Led requirements analysis, Agile task management, technical documentation, and stakeholder demos

End-to-end ML workflow on the Kaggle credit risk dataset.

• Data preprocessing, EDA, feature engineering, and model selection
• Experiment tracking with MLflow, model serialized with Pickle
• REST API for inference with FastAPI, containerized with Docker
• Endpoints validated via Swagger UI & Postman
• CI automation with GitHub Actions for reproducibility

• Built a LightGBM model tuned with Optuna
• Achieved ROC-AUC: 0.93

• Transfer learning model using Swin Transformer on MRI data (4 classes)
• Fine-tuned layers and learning rates
• Achieved weighted F1-score: 0.928

• Image classifier built with TensorFlow & AutoKeras
• Full preprocessing, inference, and evaluation pipeline with error analysis

• Collected & cleaned social media data, filtered bot-generated content
• Analyzed and visualized sentiment trends for brand mentions

• Decision Tree model predicting service purchases from behavioral data

• Statistical analysis and visualization of global population trends

Python Instructor — Algorithmics, Mindset Institute (2023 – 2026)

• Developed a curriculum for 200+ students; taught Python, data science, and ML
• Delivered training for children, college graduates, and corporate participants

Data Scientist — Prodigy InfoTech, CodeAlpha (2025)

• Data preprocessing, EDA, and ML model development on structured datasets

• Kaggle: Introduction to Machine Learning, Pandas
• Coursera: Python for Data Visualization (Matplotlib & Seaborn), Supervised Machine Learning
• Huawei: HCCDA - AI (Huawei Certified Cloud Developer Assistant – AI)
• BTK Academy: TensorFlow, Network Technologies, Scrum, Datathon 2025/2026, AutoML
• SoftITO: 320+ hour intensive program in end-to-end AI, data engineering, and MLOps

B.Sc. Computer Engineering (English) — Celal Bayar University (2020 – 2024) GPA: 3.14 / 4.00 — Coursework in computer science, statistics, mathematics, and algorithms

• 🇹🇷 Turkish: Native Speaker
• 🇬🇧 English: C2

• ALES: 88
• IELTS Academic: 8.5
• YDS: 92.5

• Build production-ready, scalable ML & LLM systems
• Work on ETL & ELT pipelines
• Work on real-world AI problems end-to-end, from data to deployment
• Grow into Machine Learning / AI Engineering roles globally