Machine Learning and AI Engineer

As a seasoned AI and Data Engineer I harness technology to transform unstructured data into actionable insights, driving significant business value. My expertise lies in deploying LLMs and automating operations, unlocking new growth opportunities.

• Data Science13 J.
• Generative KI
• Produktmanagement1 J.
• Amazon Web Services (AWS)5 J.
• Apache Spark
• GPT5 J.
• Langchain5 J.
• Microsoft Azure1 J.
• PyTorch5 J.
• R (Programmiersprache)8 J.

I thrive on solving complex data challenges and providing comprehensive support throughout the model development and deployment lifecycle. Beyond fulfilling technical requirements, my expertise encompasses product development aspects, such as strategic evaluation, backlog prioritization, and seamless UI/UX integration.

Frameworks and Libraries:
- PyTorch: Deep Learning Framework for neural networks
- Hugging Face Transformers: Library for state-of-the-art Natural Language Processing (NLP) models
- SpaCy: Library for advanced NLP tasks
- LangChain: Framework for developing applications with Large Language Models (LLMs)
- Apache Spark: Distributed data processing
- TensorFlow: Another popular Deep Learning Framework
- Keras: High-level API for neural networks, often used in conjunction with TensorFlow
- Scikit-learn: Extensive library for Machine Learning in Python

MLOps:
- Cloud Computing: Use of cloud platforms for data processing and model training (e.g. AWS, Azure, GCP)
- Databricks: Azure native data pipeline components
- Containerization (Docker, Kubernetes): Isolation and scaling of applications and ML models
- CI/CD for code and data artefacts, git tooling, GitHub actions
- Automation to streamlining repetitive tasks, accelerating development/deployment and improving overall quality
- Real-time data processing: Processing of data streams in real time (e.g. Apache Kafka, Apache Flink)

Data Engineering Concepts:
- Data Pipelines: Automated processing and transformation of data
- ETL (Extract, Transform, Load): Process for data integration from various sources
- Feature Engineering: Selection, transformation and creation of relevant features for ML models

Statistics and Data Science
- Consulting on statistical methods: Selection and application of suitable statistical methods
- Data preparation: Cleaning, transformation and integration of data
Exploratory Data Analysis (EDA): Examination and visualization of data to gain insights
- Business Intelligence: Use of data to support business decisions
- Big Data Analytics: Processing and analysis of large amounts of data
- Databases: Storage and retrieval of structured, unstructured or rapidly changing data
- Visualizations (D3.js, plotly.js, Streamlit): Creation of meaningful charts and graphs
- Reporting (Web, Dashboards, PowerBI): Presentation of results and findings in various formats

Software development in:
- Python
- R
- JavaScript
- TypeScript
- Rust