Find the perfect Machine Learning Engineer in Munich matched in minutes from over 15,000 CVs with the power of AI.
Access top freelance AI professionals specializing in deep learning, computer vision, natural language processing, and MLOps pipelines. We match you with vetted, available experts tailored to your project requirements.
About the role
Deploying AI Models in Munich Industrial and Tech Landscapes
Freelance machine learning engineers bridge the gap between data science and software engineering. In the Munich tech ecosystem, they design, build, and deploy production-ready machine learning systems for automotive, manufacturing, and enterprise software clients.
Key Technical Competencies
- Designing deep learning architectures with PyTorch and TensorFlow
- Setting up robust MLOps pipelines using Kubeflow, MLflow, and Docker
- Deploying models to cloud environments like AWS, Azure, and Google Cloud
- Optimizing neural networks for edge devices and IoT applications
- Implementing natural language processing and computer vision algorithms
Why Hire Freelance Machine Learning Specialists
Engaging external consultants allows Munich companies to accelerate AI initiatives without the long onboarding cycles of permanent hiring. External experts bring specialized knowledge from various industries to solve specific bottlenecks, such as migrating legacy models to modern cloud architectures or setting up initial training pipelines.
Collaborative Models and Language Requirements
Most projects in the local region operate on a hybrid model, combining remote development with occasional on-site workshops in Munich. While international teams communicate primarily in English, integration with local engineering departments often benefits from professionals who also understand German business contexts.
Meet FRATCH Machine Learning Engineers
Michael Nelz
Senior ML Engineer | AI Engineer | Problem Solver
Last position:
Senior ML Engineer, AI Engineer at Lanxess AG
- Deployment and scaling of existing ML initiatives, including demand and cash flow forecasts.
- Building robust monitoring with mlflow for data stability, model performance, and drift detection, as well as implementing additional ML use cases.
- Further development of an Agentic AI chatbot for transparent and easy-to-understand model explanations.
Philipp Grunert
Machine Learning & Data Engineer
Last position:
Data Scientist & ML Engineer at Data-Science Factory GmbH
- Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
- Implementation of automated end-to-end cloud processes
- Development of LLM and NLP models
- Creation of interactive reports
- Support for national and international large corporations as well as medium-sized companies in implementing ML projects
Philipp Grunert
Machine Learning & Data Engineer
Last position:
Data Scientist & ML Engineer at Data-Science Factory GmbH
- Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
- Implementation of automated end-to-end cloud processes
- Development of LLM and NLP models
- Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
- Implementation of automated end-to-end cloud processes
- Development of LLM and NLP models
- Creation of interactive reports
- Support for national and international large corporations as well as medium-sized companies in implementing ML projects
- Creation of interactive reports
- Support for national and international large corporations as well as medium-sized companies in implementing ML projects
Thomas Hoefkens
Senior MLOps, DevOps Engineer
Last position:
Senior MLOps, DevOps Engineer at Trianel Energy
- Built and operated an end-to-end MLOps platform on Azure ML and Kubernetes (Kubeflow) for automated deployment, monitoring, and scaling of forecasting models (including Temporal Fusion Transformer, Informer, and Autoformer).
- Implemented CI/CD pipelines in Azure DevOps for the full ML lifecycle – from resource provisioning (Terraform) and data transformation (Hugging Face Datasets, Pandas, PyTorch, CUDA cluster) to training and evaluation, model registry, and endpoint deployment.
- Integrated MLflow for experiment tracking, model versioning, performance monitoring, and automated registration in the Azure Model Registry.
- Developed and containerized PyTorch training jobs (Azure Notebook, Jupyter Notebooks) for price and time series forecasting (PFC models) with automatic rollout via Azure ML Endpoints and REST/gRPC interfaces, Docker containerization, secured with OAuth 2.0.
- Set up monitoring and alerting mechanisms (Prometheus, MLflow Metrics), centralized logging, and cost monitoring.
- Automated infrastructure provisioning and model deployment using Terraform, Helm, and Azure CLI; connected to existing market data systems and event pipelines.
- Migrated existing workloads and databases (IONOS → Azure, MongoDB) and integrated them into central MLOps workflows and internal networks.
- Extended the platform with LLM-based tools (LangChain, LangServe) to integrate GPT-based analysis modules into existing Spring Boot services for market anomaly detection and automated reports.
- Analyzed and designed a software solution to efficiently process large volumes of data (>3000 messages/sec) (market data store).
- Developed Spring Boot / Java 21 container services with RabbitMQ to distribute exchange data through MongoDB (Kubernetes), with fast storage of data in Redis RMaps, deduplication, forwarding messages to Read Model queues, and building Read Models for UI display in MongoDB.
- Integrated RESTHeart to create a REST API for MongoDB.
- Built an Angular frontend to simplify data queries and master data maintenance.
- Agentic coding with remote and local LLMs (Claude Sonnet, Ollama Qwen) and MCP servers.
- Developed Python scripts to transform and clean incoming exchange data (Pandas, scikit-learn).
Thomas Hoefkens
Senior MLOps, DevOps Engineer
Last position:
Senior MLOps, DevOps Engineer at Trianel Energy
- Built and operated an end-to-end MLOps platform on Azure ML and Kubernetes (Kubeflow) for automated deployment, monitoring, and scaling of forecasting models (including Temporal Fusion Transformer, Informer, and Autoformer).
- Built and operated an end-to-end MLOps platform on Azure ML and Kubernetes (Kubeflow) for automated deployment, monitoring, and scaling of forecasting models (including Temporal Fusion Transformer, Informer, and Autoformer).
- Implemented CI/CD pipelines in Azure DevOps for the full ML lifecycle – from resource provisioning (Terraform) and data transformation (Hugging Face Datasets, Pandas, PyTorch, CUDA cluster) to training and evaluation, model registry, and endpoint deployment.
- Integrated MLflow for experiment tracking, model versioning, performance monitoring, and automated registration in the Azure Model Registry.
- Developed and containerized PyTorch training jobs (Azure Notebook, Jupyter Notebooks) for price and time series forecasting (PFC models) with automatic rollout via Azure ML Endpoints and REST/gRPC interfaces, Docker containerization, secured with OAuth 2.0.
- Set up monitoring and alerting mechanisms (Prometheus, MLflow Metrics), centralized logging, and cost monitoring.
- Automated infrastructure provisioning and model deployment using Terraform, Helm, and Azure CLI; connected to existing market data systems and event pipelines.
- Migrated existing workloads and databases (IONOS → Azure, MongoDB) and integrated them into central MLOps workflows and internal networks.
- Extended the platform with LLM-based tools (LangChain, LangServe) to integrate GPT-based analysis modules into existing Spring Boot services for market anomaly detection and automated reports.
- Analyzed and designed a software solution to efficiently process large volumes of data (>3000 messages/sec) (market data store).
- Developed Spring Boot / Java 21 container services with RabbitMQ to distribute exchange data through MongoDB (Kubernetes), with fast storage of data in Redis RMaps, deduplication, forwarding messages to Read Model queues, and building Read Models for UI display in MongoDB.
- Integrated RESTHeart to create a REST API for MongoDB.
- Built an Angular frontend to simplify data queries and master data maintenance.
- Agentic coding with remote and local LLMs (Claude Sonnet, Ollama Qwen) and MCP servers.
- Developed Python scripts to transform and clean incoming exchange data (Pandas, scikit-learn).
- Implemented CI/CD pipelines in Azure DevOps for the full ML lifecycle – from resource provisioning (Terraform) and data transformation (Hugging Face Datasets, Pandas, PyTorch, CUDA cluster) to training and evaluation, model registry, and endpoint deployment.
- Integrated MLflow for experiment tracking, model versioning, performance monitoring, and automated registration in the Azure Model Registry.
- Developed and containerized PyTorch training jobs (Azure Notebook, Jupyter Notebooks) for price and time series forecasting (PFC models) with automatic rollout via Azure ML Endpoints and REST/gRPC interfaces, Docker containerization, secured with OAuth 2.0.
- Set up monitoring and alerting mechanisms (Prometheus, MLflow Metrics), centralized logging, and cost monitoring.
- Automated infrastructure provisioning and model deployment using Terraform, Helm, and Azure CLI; connected to existing market data systems and event pipelines.
- Migrated existing workloads and databases (IONOS → Azure, MongoDB) and integrated them into central MLOps workflows and internal networks.
- Extended the platform with LLM-based tools (LangChain, LangServe) to integrate GPT-based analysis modules into existing Spring Boot services for market anomaly detection and automated reports.
- Analyzed and designed a software solution to efficiently process large volumes of data (>3000 messages/sec) (market data store).
- Developed Spring Boot / Java 21 container services with RabbitMQ to distribute exchange data through MongoDB (Kubernetes), with fast storage of data in Redis RMaps, deduplication, forwarding messages to Read Model queues, and building Read Models for UI display in MongoDB.
- Integrated RESTHeart to create a REST API for MongoDB.
- Built an Angular frontend to simplify data queries and master data maintenance.
- Agentic coding with remote and local LLMs (Claude Sonnet, Ollama Qwen) and MCP servers.
- Developed Python scripts to transform and clean incoming exchange data (Pandas, scikit-learn).
Raghu Ram Vadali
Telco Customer Churn Prediction – End-to-End ML Pipeline
Last position:
Telco Customer Churn Prediction – End-to-End ML Pipeline at Self-Initiated Project
- Designed and implemented a full machine learning pipeline for churn prediction using the Telco dataset.
- Applied preprocessing techniques including missing value handling, categorical encoding, feature scaling, and PCA.
- Built and compared over 15 models (logistic regression, random forest, XGBoost, etc.) and evaluated them using accuracy, precision, recall, F1 score, ROC AUC, and PR AUC.
- Tuned hyperparameters with GridSearchCV, achieving 80.6% accuracy with random forest and XGBoost.
- Created visual reports (bar plots, heatmaps, radar charts) to interpret model performance and churn drivers.
- Exported reusable pipelines and trained models with joblib for deployment.
Raghu Ram Vadali
Telco Customer Churn Prediction – End-to-End ML Pipeline
Last position:
Telco Customer Churn Prediction – End-to-End ML Pipeline at Self-Initiated Project
- Designed and implemented a full machine learning pipeline for churn prediction using the Telco dataset.
- Applied preprocessing techniques including missing value handling, categorical encoding, feature scaling, and PCA.
- Designed and implemented a full machine learning pipeline for churn prediction using the Telco dataset.
- Applied preprocessing techniques including missing value handling, categorical encoding, feature scaling, and PCA.
- Built and compared over 15 models (logistic regression, random forest, XGBoost, etc.) and evaluated them using accuracy, precision, recall, F1 score, ROC AUC, and PR AUC.
- Tuned hyperparameters with GridSearchCV, achieving 80.6% accuracy with random forest and XGBoost.
- Created visual reports (bar plots, heatmaps, radar charts) to interpret model performance and churn drivers.
- Exported reusable pipelines and trained models with joblib for deployment.
- Built and compared over 15 models (logistic regression, random forest, XGBoost, etc.) and evaluated them using accuracy, precision, recall, F1 score, ROC AUC, and PR AUC.
- Tuned hyperparameters with GridSearchCV, achieving 80.6% accuracy with random forest and XGBoost.
- Created visual reports (bar plots, heatmaps, radar charts) to interpret model performance and churn drivers.
- Exported reusable pipelines and trained models with joblib for deployment.
Mohamed Saleh
Machine Learning Engineer (Part Time)
Last position:
Machine Learning Engineer (Part Time) at E.ON Digital Technology
- Designed and implemented an advanced, agentic RAG pipeline using LangChain and LangGraph for structured data extraction from PDFs, utilizing tools, state management, and OpenAI LLMs (GPT-4) to improve accuracy and handle complex document structures.
- Developed a Google AI agent for extraction of structured information from PDF documents and deployed the agent on Vertex AI.
- Architected data pipelines using Azure Data Factory and Databricks to ingest data from Azure Blob Storage, process it with PySpark, and load it into Azure SQL Database via Linked Services.
- Containerized AI agents and services using Docker for consistent local development and deployment.
- Utilized PySpark and Dask for database querying in coordination with Azure Blob Storage and Document Storage.
- Created a ReAct agent that extracts structured data from PDF documents using tools and integrating Azure Document Intelligence.
- Contributed to the CPO invoices validation check project using Databricks to find existing CDRs and calculate total valid costs.
- Developed a conversational AI agent (chatbot) with a FastAPI backend, integrating RAG for precise tariff extraction and deployed the service using Azure Container Apps.
- Tools used: Azure, Azure OpenAI, Azure Document Intelligence, Azure Blob Storage, Google ADK, Google Cloud, Vertex AI, Gemini, Databricks, LangChain, LlamaIndex Ollama, Docker, PySpark, Azure SQL, Azure Data Factory, Azure AI Agent, Microsoft SQL Server
Mohamed Saleh
Machine Learning Engineer (Part Time)
Last position:
Machine Learning Engineer (Part Time) at E.ON Digital Technology
- Designed and implemented an advanced, agentic RAG pipeline using LangChain and LangGraph for structured data extraction from PDFs, utilizing tools, state management, and OpenAI LLMs (GPT-4) to improve accuracy and handle complex document structures.
- Designed and implemented an advanced, agentic RAG pipeline using LangChain and LangGraph for structured data extraction from PDFs, utilizing tools, state management, and OpenAI LLMs (GPT-4) to improve accuracy and handle complex document structures.
- Developed a Google AI agent for extraction of structured information from PDF documents and deployed the agent on Vertex AI.
- Architected data pipelines using Azure Data Factory and Databricks to ingest data from Azure Blob Storage, process it with PySpark, and load it into Azure SQL Database via Linked Services.
- Containerized AI agents and services using Docker for consistent local development and deployment.
- Utilized PySpark and Dask for database querying in coordination with Azure Blob Storage and Document Storage.
- Created a ReAct agent that extracts structured data from PDF documents using tools and integrating Azure Document Intelligence.
- Contributed to the CPO invoices validation check project using Databricks to find existing CDRs and calculate total valid costs.
- Developed a conversational AI agent (chatbot) with a FastAPI backend, integrating RAG for precise tariff extraction and deployed the service using Azure Container Apps.
- Tools used: Azure, Azure OpenAI, Azure Document Intelligence, Azure Blob Storage, Google ADK, Google Cloud, Vertex AI, Gemini, Databricks, LangChain, LlamaIndex Ollama, Docker, PySpark, Azure SQL, Azure Data Factory, Azure AI Agent, Microsoft SQL Server
- Developed a Google AI agent for extraction of structured information from PDF documents and deployed the agent on Vertex AI.
- Architected data pipelines using Azure Data Factory and Databricks to ingest data from Azure Blob Storage, process it with PySpark, and load it into Azure SQL Database via Linked Services.
- Containerized AI agents and services using Docker for consistent local development and deployment.
- Utilized PySpark and Dask for database querying in coordination with Azure Blob Storage and Document Storage.
- Created a ReAct agent that extracts structured data from PDF documents using tools and integrating Azure Document Intelligence.
- Contributed to the CPO invoices validation check project using Databricks to find existing CDRs and calculate total valid costs.
- Developed a conversational AI agent (chatbot) with a FastAPI backend, integrating RAG for precise tariff extraction and deployed the service using Azure Container Apps.
- Tools used: Azure, Azure OpenAI, Azure Document Intelligence, Azure Blob Storage, Google ADK, Google Cloud, Vertex AI, Gemini, Databricks, LangChain, LlamaIndex Ollama, Docker, PySpark, Azure SQL, Azure Data Factory, Azure AI Agent, Microsoft SQL Server
Haoyuan Chen
Software Engineer – Backend Development
Last position:
Software Engineer – Backend Development at AICI GmbH
- Independently led backend development as the sole contributor and applied computer vision techniques to transform raw SLAM (Simultaneous Localization and Mapping) data into user-friendly CAD models, advancing the product from prototype to release-ready for architectural applications
- Developed and implemented mathematical algorithms to accurately detect room contours and improve the precision of wall-length estimations from spatial maps
- Contributed to reducing human intervention by optimizing backend processes for real-time, automated CAD generation
- Collaborated with cross-functional teams in robotics, data science, and software engineering to enhance system efficiency and scalability
- Stack: Python, C++, OpenCV, NumPy
Discover over 15,000 top freelancers
Machine Learning Engineers statistics
Aggregated from the professional profiles of matched freelancers.
Experience
16 years
Position duration
2.5 years
Positions per freelancer
9
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Automotive, Education
Certification focus areas
Business Intelligence, Information Technology, Legal
Bachelor's degree or higher
100%
Master's degree or higher
100%
Certifications per freelancer
3
Most common languages
German, English, French
Speak two or more languages
100%
Daily Rate Distribution
The chart shows how the daily rates of freelancers in this role are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Average rates for Machine Learning Engineers & Seniority distribution
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Frequently Asked Questions
Questions in mind? Get key insights about FRATCH
While a data scientist focuses on statistical analysis and building initial prototypes, a machine learning engineer specializes in scaling, optimizing, and deploying those models into production. They write clean, production-ready code and integrate algorithms into the broader software architecture.
Most companies in the region rely on AWS, Azure, or Google Cloud Platform. A local MLOps specialist will configure these cloud environments to orchestrate training pipelines, manage data storage, and host models securely.
Most freelance machine learning engineers work in a hybrid setup, combining remote software development with on-site alignment meetings at the client office in Munich. This hybrid approach ensures smooth integration with internal IT security standards and local development teams.
A top-tier machine learning programmer is evaluated by their portfolio of successfully deployed models and their understanding of software engineering best practices. Look for candidates who emphasize testing, model monitoring, and continuous integration pipelines rather than just model accuracy.
Yes, an experienced AI developer working in Germany is familiar with GDPR requirements and ethical AI principles. They design systems that respect data privacy, anonymize training data when necessary, and ensure secure model deployment.
Python is the industry standard for most projects, which is why every Python ML specialist has a deep command of its scientific ecosystem. For performance-critical applications, especially in the Munich automotive and robotics sectors, some professionals also utilize C++ for edge deployment.
Hiring a freelance machine learning developer provides immediate access to niche expertise for short-term projects, such as building a proof of concept or setting up an infrastructure pipeline. It avoids the prolonged recruitment cycles typical of the highly competitive Munich tech market.
A professional ML infrastructure engineer will implement tools like DVC for data versioning, MLflow for experiment tracking, and Prometheus for monitoring model drift in production. These tools ensure that AI systems remain reliable and maintainable over time.
The average hourly rate for Machine Learning Engineers in Munich is 87 €, which corresponds to a daily rate of about 692 € based on an 8-hour working day.
Of the freelancers working as Machine Learning Engineers in Munich, 100% hold at least a Bachelor's degree and 100% hold at least a Master's degree.
On average, freelancers working as Machine Learning Engineers in Munich have 16 years of professional experience, with a single engagement typically lasting around 2.5 years.
The most common languages among freelancers working as Machine Learning Engineers in Munich are German (100%), English (100%), and French (33%).
The most common industries among freelancers working as Machine Learning Engineers in Munich are Information Technology (83%), Automotive (50%), and Education (50%).
The most common business areas among freelancers working as Machine Learning Engineers in Munich are Information Technology (100%), Product Development (100%), and Research and Development (83%).
FRATCH Machine Learning Engineers main locations
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Berlin Hamburg 
Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a Free Demo
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Philipp Thomaschewski
FRATCH CEO
