Find the best Machine Learning Engineers in Germany in minutes from 15,000 CVs with AI precision.
Build production models, tune MLOps pipelines, and ship NLP, computer vision, or forecasting systems with freelancers who fit your stack and delivery needs. Get fast, precise matching with vetted, available experts.
About the role
What they build
Machine learning engineers turn data into production systems. They do not stop at notebooks. They build, train, test, and deploy models that can run in real products and business workflows.
- Model pipelines for training, evaluation, and deployment
- APIs and services that expose predictions to other teams
- Monitoring for drift, quality, latency, and failures
- Reproducible experiments and clear handover for product teams
Core stack
A strong machine learning engineer works across Python, SQL, and common ML frameworks such as PyTorch, TensorFlow, and scikit-learn. They also know how to work with cloud services, containers, feature stores, and CI/CD setups.
They should understand data engineering basics, model versioning, and how to move work from research to production without breaking the system. In Germany, this often means working with product, data, and platform teams in parallel, sometimes in English, sometimes in German.
Typical use cases
Companies bring in a machine learning engineer when a model is ready for real use, but the team lacks the production skills to ship it safely. That includes recommendation systems, fraud detection, demand forecasting, ranking, anomaly detection, and NLP features.
- Turn an offline prototype into a stable service
- Improve an existing model that is slow, brittle, or hard to maintain
- Set up training and deployment workflows
- Connect data sources, model outputs, and business tools
What strong freelancers do
Top freelancers do more than code. They ask about data quality, business goals, deployment limits, and who will own the system after launch. They write clean, testable code and make trade-offs visible.
They also know when a data scientist, software engineer, or MLOps specialist is needed next. That matters when the project mixes model work with backend integration, cloud infrastructure, or operational support.
Tools and methods
The best machine learning engineers can work in modern stacks without needing a long ramp-up. They are comfortable with notebook-based exploration, then hardening the solution for production.
- Python, SQL, Git, Docker, and Linux
- PyTorch, TensorFlow, scikit-learn, MLflow, and Airflow
- AWS, Azure, or Google Cloud
- REST APIs, batch jobs, and model monitoring
When freelance makes sense
Freelance support is a good fit when a company needs focused delivery, not a long hiring process. It also helps when a project has a clear scope, a fixed deadline, or a gap in the team’s machine learning and deployment skills.
For companies in Germany, freelancers are often useful when local stakeholders need on-site workshops but the build itself can happen remotely. That mix works well for product teams, industrial companies, insurers, and software firms that need practical execution, not research-only work.
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.
Katharina Schmidt
ML Engineer & Data Scientist | Python
Last position:
Virtual staining at Faculty of Electrical and Computer Engineering, TU Dresden
- Technical and professional management of software and ML development; largely independent implementation of programming and guidance of the team and external project partners
- Design, creation, and preparation of training and test data sets from experimental image data and simulations
- Selection, implementation, training, validation, and testing of neural networks for image-based reconstruction and transformation
- Systematic evaluation, comparison, and optimization of various model architectures (convolutional neural networks, generative adversarial networks, autoencoders, transformers)
- Design and implementation of explainable AI analyses for model interpretability and robustness assessment (analysis of feature maps, augmentation studies, guided backpropagation)
- Presentation of the developed methods and results in project meetings and at international conferences
David Onaiyekan
ML Engineer
Last position:
Research Intern at Pattern Recognition Lab
- Spearheaded the integration of a custom Transformer-based encoder into the AFFGANwriting pipeline, replacing the legacy VGG19 architecture to capture richer, high-fidelity writer-style representations.
- Boosted user-study pick-rates by 40%, demonstrating a significant leap in the perceptual quality and realism of the generated handwriting compared to the baseline model.
- Enhanced OCR performance by 20% by implementing a teacher-student framework that leveraged a TrOCR benchmark model for auxiliary training alignment
Amr Amer
Machine Learning Engineer
Last position:
Machine Learning Engineer at German Research Center for Artificial Intelligence (DFKI)
- Developed end-to-end reproducible ML pipelines (PyTorch) with data versioning (DVC), experiment tracking (MLflow), automated testing (PyTest), and CI/CD across all training workflows.
- Scaled Vision Transformer and CNN training across NVIDIA A100 GPU clusters (CUDA, DDP, SLURM); applied hyperparameter optimization (W&B Sweeps) to reduce training overhead and identify optimal configurations.
- Developed a real-time 3D human motion generation system (ViT, VQ-VAE, SMPL-X/PIXIE) for personality-conditioned avatar synthesis; achieved state-of-the-art FID = 6.15 and P-FID = 10.31 on the UDIVA benchmark.
- Validated model expressiveness through structured user studies, achieving 86% accuracy in distinguishing extroverted vs. introverted avatar behaviors.
- Optimized inference pipelines by deploying PyTorch models via TensorRT and ONNX Runtime into native C++ code; benchmarked performance.
Deepak Reddy Narra
AI Engineer
Last position:
Machine Learning Engineer at go AVA GmbH
- Designed and built a multi-tenant Python/Flask API platform with JWT + API-key authentication, scoped access control, and service-level orchestration as the backbone for AI applications.
- Built a multimodal RAG system with hybrid chunking, dense/sparse embeddings, hybrid retrieval, reranking, and vector search to deliver grounded, high-precision responses across enterprise data.
- Productionized AI workflows with Docker, CI/CD, Redis-backed async job tracking, webhook callbacks, external AI/media service integrations, and runtime health/reliability controls.
Daniel Christoph
AI Engineer
Last position:
AI Engineer at EMLI GmbH
- Development and deployment of AI/ML models to support research, production, and QC processes in GxP-regulated life science environments
- Building scalable MLOps infrastructures for the production use of AI solutions in regulated areas, including cloud architectures and data pipelines
- Regulatory compliance and validation according to GAMP 5, EU AI Act, and data integrity requirements, including audit trail-compliant documentation
- Interdisciplinary project management in AI and digitalization projects: coordinating stakeholders, budget responsibility, client communication
- Data engineering and integration: analyzing diverse production data, ensuring data quality, and integration into validated systems
Philipp Großer
Machine Learning Engineer
Last position:
Machine Learning Engineer at docmetric GmbH
- Analyzed patient data for various clients
- Developed complex analysis pipelines
- Performed quality assurance on methods
Louis Guitton
Freelance Solutions Architect and Machine Learning Engineer
Last position:
Freelance Solutions Architect and Machine Learning Engineer at Self-employed
- Develop and demonstrate solutions using GenAI software like langchain, vercel ai sdk, copilotkit
- Work with customers to understand their challenges and provide the best solutions based on open-source data products
- Build RAG and GraphRAG solutions using Neo4j, lancedb, and Postgres
- Deploy a LLMOps platform using kubernetes, terraform, helmfile, Arize phoenix, mlflow
- Architect and build data pipelines using dbt, Trino, Spark, Iceberg, Airflow, ArgoCD, terraform, kubernetes
- Delivered user-centred technical strategy for Agriculture 4.0 and precision livestock farming, helping my client secure funding from Bpifrance
- Delivered a prospecting tool for a leading French solar carport installer, using geospatial computing (GIS), speeding up the sales process
- Built digital twin architecture for solar carports and EV chargers, making real-time monitoring and smart charging possible
Julian Wergieluk
CEO & ML Engineer
Last position:
CEO & ML Engineer at LOLML GmbH
- Consulting: Monte-Carlo scenario analysis of an exotic swaps portfolio (C++, bash, gnuplot)
- Project selection: Industrial Process Optimization, EDI-Automation, LLM Explainability, Portfolio Optimization, Time-Series
Marcel Meyer
Cloud-Architect, Senior Solution Architect, Senior Software-Engineer
Last position:
Cloud-Architect, Senior Solution Architect, Senior Software-Engineer at Assignment of KPIs for the service landscape to record and analyse costs per user
- Technologies: GoLang, JavaScript, TypeScript, AWS, Terraform, Git
- Conception of AWS infrastructure and existing services
- Analysis of IAM accounts and roles
- Setup of Cost Explorer and CloudWatch monitoring
- Setup of DynamoDB and S3 persistence of collected information
- Reporting and cost calculation
- Conception of Terraform deployment
Mohamed Hassan
Machine Learning Engineer Intern
Last position:
Machine Learning Engineer Intern at Kautex Textron
- Developed deep learning models for industrial shape optimization
- Built deployment pipelines for production-ready ML systems
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
Meisam Ghafarlangroudi
Machine Learning Engineer
Last position:
Machine Learning Engineer at Geeks
- Utilized a Large Language Model (LLM) at WordUp, tailored to enhance vocabulary learning by understanding and generating contextual examples, improving personalized learning experiences
- Developed a high-performance Fast API service for retrieving high-K similar vectors with batch querying capabilities. This service is crucial for enabling efficient Retrieval Augmented Generation (RAG) and semantic search applications
- Designed and implemented a high-performance Python ETL pipeline, optimizing CPU and I/O utilization and streamlining data cleansing logic, resulting in a 30% reduction in processing time
- Utilized machine learning to analyze user behavior and predict churn, identifying key engagement trends that led to a 15% increase in user retention and satisfaction
- Developed a Customer Lifetime Value (CLTV) prediction model, leading to a 10% increase in average CLTV through targeted retention efforts
Sean Schenefelder
Part-time Machine Learning Engineer
Last position:
Independent Business / Freelancing
- Development of various commercial software projects (mostly in the context of AI)
David Forino
CTO and co-founder
Last position:
CTO and co-founder at Slected.me GmbH
- Combined artificial intelligence and real job market data to provide personalized market worth based on skills and experience
Discover over 15,000 top freelancers
Machine Learning Engineers statistics
Typical experience
10 years
Average project duration
2.2 years
Certifications per freelancer
3
Top business areas
Information Technology, Research and Development, Product Development
Top industries
Information Technology, Education, Professional Services
Most common languages
German, English, Arabic
Bachelor's degree or higher
93%
Master's degree or higher
87%
Doctorate
7%
Salary / 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
Looking for clear information? Everything important about FRATCH is here
A Machine Learning Engineer builds models that can run in production, not just in a notebook. The work usually covers data preparation, training, evaluation, deployment, and monitoring. In practice, that can mean an API, a batch scoring job, or a full ML pipeline tied to a product.
Look for strong Python and SQL skills, plus hands-on experience with frameworks such as PyTorch, TensorFlow, or scikit-learn. A good freelancer also understands cloud setups, containers, testing, and model operations. The best candidates can explain trade-offs in data quality, latency, and maintainability.
A Machine Learning Engineer focuses on building and shipping models into real systems. A data scientist is often more focused on analysis, experimentation, and insight generation, while an MLOps engineer may focus more on deployment infrastructure and operations. In smaller teams, one freelancer may cover parts of all three.
Freelance support makes sense when you need specialist execution for a defined project or a short-term capability gap. It is also useful when a team already has data science or backend talent, but lacks production ML experience. That keeps delivery moving without adding a long permanent hiring cycle.
Most Machine Learning Engineers can work remotely for the build, review, and deployment work. On-site time helps when there are workshops with product, data, or leadership teams, especially if the project needs close alignment on business rules or data access. Many German companies use a hybrid setup for that reason.
Expect code, not just slides. A strong freelancer should leave behind a working training pipeline, a deployable model service or batch process, evaluation results, and clear documentation. They should also make it easy for your team to maintain the solution after handover.
Judge quality by production readiness, not only model accuracy. Good work is stable, reproducible, monitored, and integrated with your systems. If the freelancer can explain failure modes, data assumptions, and rollback options clearly, that is a strong sign.
German companies in manufacturing, mobility, retail, insurance, finance, and software often need this role. They use it for forecasting, quality checks, personalization, risk scoring, and process automation. The best freelancers can adapt to industry data and work with both technical and business teams.
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Philipp Thomaschewski
FRATCH CEO
