Freelancer profile translated to English.

Description

I am an experiencedAI EngineerandMachine Learning Scientistwith extensive expertise in developingAI and ML modelsfor companies in **Technology, Construction, Retail, Insurance, and Banking**. My specialization lies in developing AI-powered applications in **Machine Learning (ML)**, **Large Language Models (LLMs)**, and **Natural Language Processing (NLP)**. I have profound experience in implementing **complex ML solutions**, **chatbots**, and **AI agents in Python**, as well as integrating AI systems into **cloud environments**.

I am happy to support you with projects in the following areas:

AI software development
AI agents and agentic workflows
Development of Machine Learning models with Python
Large Language Models (LLM) and LLM-based applications
Natural Language Processing (NLP)

Additionally, I develop custom AI components with Large Language Models (LLMs) for your use case, for example:

Fine-tuning of open-source LLMs on company data
AI agents for workflow automation
Chatbots for information and document search for employees
Voice bots/voice assistants for apps or APIs
Classification and information extraction from documents

Project Highlights:

AI product with agentic workflows and LLM fine-tuning for 6 million users
Locally hosted enterprise GPT for confidential documents and standards in engineering
Machine learning software for automated detection of leaks in water pipes
Voice assistant for a beekeeping app for hive monitoring

Expertise:

Development and training of ML models
Speech Recognition
Chatbots / Voice bots
LLMs
AI Agents and Agentic Workflows
Knowledge Graphs
RAG / GraphRAG
Supervised Fine-Tuning
Metrics & Evaluation
Deployment
MLOps / LLMOps

Technologies:

Python
PyTorch
TensorFlow
LangChain / LangGraph
Azure
Google Cloud
OpenAI
Neo4j

Industry field of expertise

Languages

German
Native or bilingual
English
Fluent

Workplace preferences

Remote only

Primarily works remotely

Ingenieur- und Planungsgesellschaft im Bauwesen
Local Enterprise GPT for Confidential Documents [AI & Machine Learning Engineer]
May 2025 - January 2026 (8 months)
Goal:Development and deployment of a fully locally hosted AI chatbot that searches, links, and provides confidential company documents as a secure knowledge system to employees via Retrieval-Augmented Generation (RAG).

Tasks and Achievements:
Deployment and optimization of open-source LLMs on company's own server architecture (NVIDIA RTX 5090)
Development of an AI application with chatbot, vector search, and re-ranking
Implementation of hybrid search, HyDE, and multi-query retrieval to enhance response quality
Data preparation, document parsing, and loading of vector databases
Building scalable MLOps pipelines for operation, monitoring, and updates
MLOps LLMs Artificial Intelligence (AI) Chatbots AI
International tätiges Bauunternehmen
Enterprise GPT for Building Planning and Construction [AI Engineer]
May 2024 - October 2024 (6 months)
Goal:Development of a chatbot for planners, architects, and engineers to interact with data on the construction, planning, and management of buildings and structures.

Tasks and Achievements:
Successful development of a chatbot with RAG pipelines to access multiple data sources of the construction company
Conception, planning, and coordination with IT and business departments
Automation of business processes with agentic workflows
Deployment in Azure, including Azure App Service and Azure OpenAI
Artificial Intelligence (AI) Microsoft Azure OpenAI AI Agents RAG
Großer Elektronikkonzern
AI Product with Agentic Workflows and LLM Fine-Tuning for 6 Million Users [AI & ML Engineer]
April 2024 - Today (2 years and 2 months)
Goal:Development of AI-based software products that are error-free, scalable, measurable, and precisely tailored to company data and customer needs. Deployment and rollout of the AI solution for > 6 million customers.

Tasks and Achievements:
Development of training pipelines for open-source LLMs in Python
Development of agentic workflows for transforming JSON structures
Creation and implementation of evaluation pipelines and metrics
MLOps: Deployment and serving of models in Databricks and AWS
Building feedback loops for model alignment to improve quality
Successful execution of the PoC and current development towards product maturity
Databricks MLOps Python LLMs MLflow