Junior MLOps Engineer

I am a junior MLOps engineer focused on ML deployment, pipeline automation, and production-ready machine learning systems. I have hands-on experience building end-to-end ML pipelines in industrial environments and enjoy turning research and prototypes into reliable software.

At Mars GmbH, I designed and shipped a computer vision quality-inspection system that covered the full lifecycle from data ingestion and feature engineering to model training, validation, and integration into live quality-control workflows. This experience strengthened my ability to work across ML engineering, software delivery, and operational constraints.

I work extensively with Docker, FastAPI, Azure ML, MLflow, DVC, GitHub Actions, and Kubernetes. I also build reproducible workflows, modular codebases, and automated retraining and monitoring systems to support maintainable production ML.

In my current research role at TU Dortmund University, I design modular ETL pipelines for high-frequency wearable sensor data and build reusable Python libraries that standardize experiment workflows. I place strong emphasis on reproducibility, structured logging, schema validation, and pipeline reliability.

I have also completed internships in data science and ML engineering, where I delivered regression and classification pipelines, improved model performance through feature analysis and hyperparameter tuning, and contributed to production-quality ML software. These roles helped me develop a practical, end-to-end understanding of the ML lifecycle.

Outside of work, I am building an open-source MLOps portfolio that includes containerized model serving, automated retraining, and production monitoring. I am motivated by solving real-world ML infrastructure problems and creating systems that are robust, scalable, and easy to maintain.