Graph Neural Network Expert in Germany
for connected data solutions, matched in minutes with vetted professionalsHire experts who design graph-based machine learning models, build PyTorch Geometric pipelines and apply node, edge and graph classification to real business data. FRATCH connects you quickly with precise matches and vetted, available freelancers.
Meet FRATCH Experts in Germany, who have recently used Graph Neural Network
Mirza K.
Last position:
Agentic Automation and a RAG system
- This project involved extraction of intelligence data to support report writing for a company that provides geopolitical, global, commercial intelligence. The data have been gathered from a number of resources (interview transcripts, online data, internal documents), and then a knowledge base has been build from it. This was the basis of a complex RAG system, that was evaluated against a golden dataset. Agents have been used to find out the contradicting intelligence, the statements supporting each other, and to store back the generated knowledge.
Used: Python, RAG, LangGraph, LangChain, deepeval, MCP
Marco P.
Last position:
Co-founder at Health AI Language Learning Startup
Co-founded an AI-native language learning startup, defining the product vision, AI architecture and technical roadmap. Designed and built the AI and backend stack, including LLM fine-tuning pipelines, custom agentic workflows, and scalable inference infrastructure. First product currently in private beta.
Utsav R.
Last position:
Working Student Junior Data Scientist (Performance Team GT Fleet) at Uniper SE
- Analyzed large-scale power plant data to develop and optimize key performance indicators (KPIs) for fleet-wide performance monitoring.
- Designed and developed interactive Power BI dashboards to provide real-time insights into key business metrics, improving decision-making processes across departments.
- Collaborated with site engineers and asset management to harmonize performance metrics across multiple countries.
- Supported digital transformation initiatives by implementing data-driven use cases using agile project management methods.
- Utilized OSIsoft PI systems for time-series data analysis and visualization to improve operational insights.
Aravind S.
Last position:
AI – Data Specialist at Emirates Islamic Bank
- Architected and deployed LLM based AI agents, RAG pipelines, and vector search solutions for decision support across retail banking department.
- Developed and shipped robust AI pipelines with guardrails, error handling, monitoring, and fallback logic ensuring high reliability outcomes and compliance with data privacy.
- Developed and deployed ML models to identify transactional anomalies, improving fraud detection and risk assessment in high-volume datasets for credit risk modelling.
- Built, evaluated and fine-tuned ML models to generate propensity scores for customers used to drive personalized targeting campaigns for credit cards and personal finance/loan products.
- Developed an NLP pipeline using BERT embeddings and spaCy NER for SMS/email analysis and customer query logs.
- Trained machine learning models using Isolation Forest to classify user behaviour and detect anomalies.
- Extracted, cleaned, enriched and feature engineered datasets from different sources to build feature stores that powered ML model training.
- Led development of dashboards using Power BI, Grafana, and Prometheus to monitor model performances, KPI trends, and marketing metrics.
- Built multi-touch attribution models using logistic regression and time-decay weights to evaluate lead quality.
- Developed scalable ETL pipelines from CRM, T24, SAP, and ERP, supporting millions of monthly transactions.
- Integrated testing and CI/CD workflows for robust data pipeline deployment.
Kurt S.
Last position:
Lead AI Architect Solar Industry LLM Orchestration & Agents at Greencells Development Group
- Architected end-to-end agentic AI system for automated B2B solar sales with multi-step workflows, including planning, memory, and guardrails
- Led a cross-functional team to deliver a production system on schedule while maintaining compliance
- Utilized knowledge graphs and SQL
- Tech: LangChain, Pydantic AI, OpenAI/Anthropic APIs, FastAPI, Neo4j, GNNs, structured reasoning, relational data, SQL, Pandas, NumPy
Chaima D.
Last position:
Data Scientist Intern at Marelli Automotive Lighting
- Developed and deployed a deep learning model for automated keypoint detection in headlamp light distributions.
- Prepared and processed datasets, and selected VGG16 after benchmarking CNN architectures for the best accuracy efficiency trade-off.
- Delivered a Flask REST API, containerized with Docker, and integrated the solution into an existing internal system, enabling automated and efficient evaluation of headlamp designs.
Muhammad U.
Last position:
Research Assistant at Saarland University
- Applied AI-driven CADD methodologies for biosynthetic pathway optimization and molecule screening.
- Integrated synthetic biology with computational chemistry workflows for rapid in-silico experimentation.
- Automated ML pipelines using Python, PyTorch, and Scikit-learn on Linux, improving model testing and reproducibility.
Hema K.
Last position:
Data Analyst (Working Student) at Institute for Sport, University of Mannheim
- Conduct analytics on operational datasets using Python and Excel to support performance insights.
- Apply statistical analysis to identify patterns and optimize internal processes.
Sabrine K.
Last position:
Team Lead at InstaDeep
- Led a team of junior Research Engineers, providing mentorship, technical guidance, and career development support to foster their growth in deep learning and machine learning engineering.
Daniel C.
Last position:
Founder & Managing Director at BotCraft GmbH
- Building the company with a focus on connectivity for IIoT and Industry 4.0, iRPA/process automation, advanced robotics and smart systems, sensors and services
- Project management and software architecture for IoT gateway development (since 2020) with protocol translation, IT/OT convergence and GRC
- Developing RPA bots for automating and monitoring industrial processes with an agent-based AI approach (since 2020)
- Implementing unsupervised clustering and anomaly detection for time series data in big data streaming pipelines (since 2021)
- Introducing a Docker-based release train for OTA updates with DevSecOps and CI/CD (since 2018)
Maria Daniela L.
Last position:
Research Scientist at Intel Labs
- Designed and implemented AI-driven orchestration models for large-scale distributed systems.
- Developed Python and Go-based optimization pipelines to automate service placement across Kubernetes clusters.
- Worked on scalable data pipelines and production-ready model integration with serverless frameworks (intent-driven K8s operators).
- Contributed to the Horizon Europe VERGE project.
Talha T.
Last position:
API Development for Advanced CDS Analytics at Academic Project
- Developed APIs for advanced credit default swap analytics supporting both MongoDB and file-based workflows
Raksha S.
Last position:
Working Student – Industrial Foundation Model at Siemens AG
- Design and implement an end-to-end Siemens NX based pipeline to convert OBJ CAD models into graph representations by applying AI-driven clustering of mesh faces into nodes and face adjacency for edges, streamlining GNN integration
- Generate a large-scale synthetic 3D CAD dataset, annotating parts with few MFCAD-style features to ensure balanced, diverse training data for GNN workflows
- Support the design, training, and evaluation of graph neural network architectures for AI-driven detection and classification of geometric features in 3D CAD shapes, accelerating feature-recognition workflows
Adithya N.
Last position:
Vehicle Classification and Detection using Neural Networks
Detecting and classifying vehicles in images and video for traffic monitoring
- A YOLO + Faster R-CNN model built for real-world traffic and autonomous-vehicle scenarios. Awarded Best Paper Award at St Joseph Engineering College, March 2025.
What it does
- The model takes images or video frames and both localizes and classifies vehicles by type, making it usable for downstream applications such as traffic-flow monitoring or perception in autonomous-vehicle systems.
What I did
- Combined YOLO (for fast detection) with Faster R-CNN (for higher-precision classification), rather than relying on a single architecture, trading off speed and accuracy where each mattered most.
- Achieved 90% mean Average Precision (mAP), evaluated using IoU-based metrics rather than just raw accuracy, to properly reflect localization quality.
- Handled the full data processing and evaluation pipeline in Python using TensorFlow and OpenCV.
- The accompanying paper was awarded the Best Paper Award by the Department of CSE at St Joseph Engineering College.
Tech stack: Python, TensorFlow, OpenCV, YOLO, Faster R-CNN
Discover over 15,000 top freelancers
Statistics of experts using Graph Neural Network
Aggregated from the professional profiles of matched freelancers.
Experience
9 years
Position duration
1.8 years
Positions per freelancer
8
Top business areas
Information Technology, Research and Development, Product Development
Top industries
Information Technology, Education, Automotive
Certification focus areas
Information Technology, Research and Development, Business Intelligence
Bachelor's degree or higher
100%
Master's degree or higher
79%
Doctorate
36%
Certifications per freelancer
1
Most common languages
German, English, Arabic
Speak two or more languages
100%
Based on our profile pool as of 19 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Germany 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.
Average rates of experts in Germany using Graph Neural Network
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.
Calculated based on our freelancers’ daily rates as of 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Graph Neural Network experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Information Technology (79%)
- Education (57%)
- Automotive (36%)
- Media and Entertainment (29%)
- Professional Services (29%)
- Energy (21%)
- Banking and Finance (21%)
- Healthcare (21%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What it is
A Graph Neural Network (GNN) is a machine learning model built for data whose entities and relationships matter together. It represents items as nodes and connections as edges, then passes information across the graph to learn useful patterns. This makes GNNs well suited to recommendation, fraud detection, knowledge graphs and molecular analysis.
Core capabilities
GNN specialists choose architectures and training methods that fit the graph structure and business objective. Their work can include graph convolution, graph attention, message passing, embeddings, link prediction and node or graph classification. They also handle sampling, feature design and evaluation for sparse or rapidly changing graphs.
Ecosystem and tooling
The ecosystem commonly combines Python with PyTorch or TensorFlow and graph-focused libraries such as PyTorch Geometric, Deep Graph Library and NetworkX. Professionals may connect these models to Neo4j, graph data pipelines, vector search and cloud machine learning services. They also use experiment tracking, data validation and model monitoring to support reliable delivery.
Where companies use it
GNN projects appear wherever connected records contain signals that standard tabular models miss:
- Product, content and user recommendations
- Transaction networks and fraud investigation
- Knowledge graph search and entity resolution
- Drug discovery, materials research and supply chain analysis
- Traffic, logistics and infrastructure optimization
When to bring in expertise
Companies often seek freelance GNN expertise when a proof of concept must become a dependable service, when graph data is difficult to model, or when existing predictions ignore relationships between records. In Germany, specialists may support industrial, automotive, financial and research teams remotely or alongside local data groups. Clear documentation and practical English or German communication help distributed teams move efficiently.
What strong professionals deliver
Strong specialists understand both graph theory and the limits of the available data. They establish meaningful baselines, test whether graph structure improves the outcome, and prevent leakage between connected records. They can explain embeddings and predictions to stakeholders, expose assumptions, build reproducible training pipelines and prepare deployment with attention to latency, drift, privacy and explainability.
Frequently asked questions
What clients ask us most about Graph Neural Network — answered in short.
A Graph Neural Network learns from entities and the relationships between them. Companies use GNNs for recommendations, fraud detection, knowledge graph reasoning, molecular prediction, route analysis and other problems where connections carry important information.
A Graph Neural Network propagates information across connected nodes, while a standard neural network usually expects independent or regularly ordered inputs. A GNN can reveal relationship-based signals, but it also requires careful graph construction, sampling and validation.
A Graph Neural Network is a predictive machine learning approach, not a replacement for a graph database. Graph databases such as Neo4j store and query relationships, while a GNN learns representations or predictions from that connected data; projects often use both.
A strong GNN specialist should understand Python, PyTorch or TensorFlow, data engineering and model evaluation. Experience with PyTorch Geometric, Deep Graph Library, NetworkX, Neo4j, embeddings and production monitoring is useful when the model must operate in a real service.
The right Graph Neural Network experience depends on the assignment. A focused prototype may need strong modeling and data skills, while production work also calls for graph schema design, scalable training, deployment, monitoring and clear validation against non-graph baselines.
Yes. Graph Neural Network projects are often suitable for remote collaboration because data reviews, experiments and code work can be organized online. On-site sessions may still help with domain discovery, secure data access or alignment with industrial and research teams in Germany.
A quality GNN project starts by proving that graph structure adds value over credible baseline models. Look for clean separation of training and evaluation data, meaningful graph features, leakage checks, reproducible experiments, explainable outputs and a deployment plan that reflects operational constraints.
A Graph Neural Network specialist should clarify the business decision, graph definition, data freshness, target labels and success criteria before modeling. They should also ask about access controls, expected inference speed, changing relationships and whether the outcome must be explainable to customers or regulators.
The average hourly rate of freelancers in Germany who have used Graph Neural Network in their recent projects is 93 €, which corresponds to a daily rate of about 744 € based on an 8-hour working day.
Of the freelancers in Germany who have used Graph Neural Network in their recent projects, 100% hold at least a Bachelor's degree, 79% hold at least a Master's degree, and 36% hold a doctorate.
On average, freelancers in Germany who have used Graph Neural Network in their recent projects have 9 years of professional experience, with a single engagement typically lasting around 1.8 years.
The most common languages among freelancers in Germany who have used Graph Neural Network in their recent projects are German (100%), English (100%), and Arabic (29%).
The most common industries among freelancers in Germany who have used Graph Neural Network in their recent projects are Information Technology (79%), Education (57%), and Automotive (36%).
The most common business areas among freelancers in Germany who have used Graph Neural Network in their recent projects are Information Technology (100%), Research and Development (93%), and Product Development (86%).
Main locations of FRATCH Experts, who have recently used Graph Neural Network
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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