Skills

My skills span a wide range of technologies and practices, with a strong focus on:

• Generative AI,
• Application Development with Python,
• DevOps and Cloud platforms,
• Machine Learning, and
• Data Analytics.

Below is a detailed overview of my expertise, organized by category.

🤖 Generative AI

Frameworks

Framework	Description	Level
LangChain	General-purpose GenAI SDK	[■■■■□]
LangGraph	Agentic-focused GenAI SDK	[■■■□□]
Langfuse	Tracing & Monitoring SDK	[■■■□□]

Techniques

Technique	Description	Level
RAG	Retrieval-Augmented Generation	[■■■■□]
Agents	Autonomous systems using tools, memory and planning	[■■■□□]
Prompt Engineering	Crafting effective prompts for AI models	[■■■■□]
Evaluation	Assessing AI model performance	[■■□□□]

Applications

I have applied these frameworks and techniques in various projects, including:

• AI assistants as internal tools for HR and IT departments
• Chatbots for customer support (e.g. tourism)
• Q&A systems for knowledge bases and documentation

🐍 Application Development (Python)

Frameworks & Libraries

Name	Description	Level
FastAPI	Modern web framework for building APIs and backends	[■■■■□]
Pydantic	Data validation and settings management library	[■■■■□]
Gradio	UI library for building machine learning demos	[■■■□□]

Tools

Tool	Description	Level
uv	Environment and dependency management	[■■■■□]
ruff	Linting and formatting	[■■■■□]
pre-commit	Git hooks for code quality	[■■■□□]
pytest	Testing framework	[■■□□□]

Practices

• I follow best practices in Python development, including using type hints, adhering to PEP 8 standards, and implementing robust testing strategies.
• I also prioritize code quality and maintainability through CI/CD pipelines, code reviews, and comprehensive documentation.
• In Git workflows, I use conventional commits, isolated feature branches, and pull requests to ensure a clean and organized codebase.

☁ DevOps & Cloud

Platforms

Platform	Description	Level
Kubernetes / OpenShift	Container orchestration platform	[■■■■□]
Azure	Cloud computing platform by Microsoft	[■■■□□]
AWS	Cloud computing platform by Amazon	[■□□□□]

Technologies

Technology	Description	Level
GitLab CI	Continuous Integration and Deployment platform	[■■■■□]
GitHub Actions	CI/CD platform for GitHub repositories	[■■□□□]
Docker / Podman	Containerization platform	[■■■■□]
Helm	IaC and Package Management for Kubernetes	[■■■■□]
Terraform	Infrastructure as Code for Cloud resources	[■■■□□]

🧠 Machine Learning

Frameworks & Libraries

Name	Description	Level
PyTorch	Deep learning framework for building models	[■■■□□]
Transformers	Library for natural language processing tasks	[■■■■□]
scikit-learn	Machine learning library for Python	[■■■■□]
YOLO	Real-time object detection system	[■■■■□]

Fields and Techniques

• I have experience in Natural Language Processing (NLP), Computer Vision, and Time Series Forecasting.
• My expertise includes model training and fine-tuning, evaluation, and deployment, as well as feature engineering and data preprocessing.
• I am familiar with both supervised and unsupervised learning techniques, including classification, regression and clustering.

📊 Data Analytics

Frameworks & Libraries

Name	Description	Level
Pandas	Data manipulation and analysis library	[■■■■□]
NumPy	Numerical computing library	[■■■□□]
Matplotlib	Plotting library for data visualization	[■■■■□]
Geopandas	Geospatial data analysis library	[■■■□□]
networkx	Network analysis library	[■■■□□]

Techniques and Applications

• I specialize in data cleaning, transformation, and visualization, enabling insights from complex datasets.
• My work includes statistical analysis, exploratory data analysis (EDA), and feature engineering for machine learning models.
• I have applied these skills in various domains, including traffic data analysis, geospatial data processing, and housing market analysis.

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If you have further questions about my skills or would like to discuss potential collaborations, feel free to reach out via LinkedIn or GitHub.