a data enthusiast

As a data enthusiast and skilled data scientist, I am dedicated to collecting, processing,
and analyzing data to uncover insights and help others make data-driven decisions.

  • Cloud
  • Python
  • Code

about me

I am a dedicated data scientist and AI engineer with a solid foundation in computer science and machine learning. My academic journey at Philipps University Marburg equipped me with advanced expertise in statistical methods, machine learning algorithms, NLP, and information retrieval. My experience covers end-to-end AI pipelines, optimized retrieval-augmented generation (RAG) systems, and hybrid AI architectures utilizing LangChain, LlamaIndex, and CrewAI. I've successfully deployed scalable AI solutions on cloud platforms (Azure, AWS, GCP) and consistently pursue innovative methods to improve model reliability and performance. With a proven ability to deliver high-quality AI-driven solutions, I strive to leverage cutting-edge technologies to solve real-world challenges.

Areas of Interest

Take a look at some of the things I love working on

  • NLP & Generative AI

    Experienced in developing robust NLP and GenAI solutions, including retrieval-augmented generation (RAG) systems and hybrid retrieval mechanisms. Proficient in LangChain and LlamaIndex, I've designed optimized retrieval pipelines that integrate precise data, minimize hallucinations, and deliver accurate, context-aware interactions.

  • Analytics & Visualization

    I utilize visualization tools like Plotly (Dash) and Matplotlib to clearly communicate data insights, developing dashboards that drive informed business decisions.

  • Software Development

    Skilled in Python, Java, and C#, developing efficient, maintainable software solutions with a focus on data-driven applications and backend systems.

  • Machine Learning & Optimization

    Extensive hands-on experience with CNNs, RNNs, Transformers, and classical ML techniques. I specialize in pipeline optimization, model deployment, and continuous integration to ensure reliable, scalable solutions.

  • Mathematics

    Strong background in optimization, linear algebra, probability, and statistical modeling—crucial in building effective AI and ML solutions.

  • Cloud Compute

    Experienced deploying scalable AI solutions on Azure, AWS, and GCP, effectively utilizing containerization (Docker) and orchestration tools (Kubernetes) for robust, efficient deployments.