Graph Neural Network Experts in Germany
in minutes from over 15,000 CVs with the power of AI.Hire experts who turn graph data into usable models for link prediction, node classification, recommendation systems, and fraud detection. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Germany, who have recently used Graph Neural Network
Mirza Klimenta
Last position:
Agentic AI for a DeepResearch project at Freelance
- Created a multi-agentic system supported by a knowledge graph to automate drafting of research papers
- Used multiple experts (OpenAI models) collaborating during document drafting
- Extracted useful information from the knowledge graph
- Technologies: LangChain, LangGraph, Smolagents, LlamaIndex, dspy
- Infrastructure: Terraform and GitHub Actions (CI/CD) on AWS
- Deployed initial application as a Streamlit app
Utsav Rabadiya
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 Sasi Nair Purayath
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.
Marco Pennacchiotti
Last position:
Head of Data Science and Data Engineering at Entrix
- Established and leading multi-year research roadmap
- Developed and implementing hiring plan for science and data
- Spearheading data engineering efforts in the company
- Led the team to deploy a new trading algorithm, increasing assets’ revenue of 18%
Kurt Stoll
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 Dahri
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.
Hema Kumar
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 Krichen
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 Carton
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 Leite De Souza
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 Tariq
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 Shet
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 Naik
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
1.9 years
Positions per freelancer
8
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Education, Automotive
Certification focus areas
Information Technology, Research and Development, Product Development
Bachelor's degree or higher
100%
Master's degree or higher
77%
Doctorate
38%
Certifications per freelancer
1
Most common languages
German, English, Arabic
Speak two or more languages
100%
Based on our profile pool as of 30 Aug 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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
Graph basics
Graph Neural Networks, often called GNNs, are built for data where relationships matter as much as individual records. They learn from nodes, edges, and their features to find structure that standard models miss. Companies use them for recommendations, entity matching, fraud signals, and network analysis.
Where they fit
- Link prediction and similarity search
- Node and graph classification
- Recommendation and ranking systems
- Anomaly detection in connected data
- Molecular, social, and knowledge graphs
Ecosystem
Strong specialists work with PyTorch Geometric, DGL, PyTorch, TensorFlow, and common graph stores such as Neo4j or TigerGraph when the project needs them. They also know how to prepare graph schemas, sampling pipelines, and evaluation setups for large connected datasets. Good work includes clean training loops and clear model behavior.
When to bring help
Companies usually bring in freelance expertise when graph data is messy, model quality is unclear, or an existing ML team needs help moving from tabular models to graph-based approaches. This is common in fraud, search, logistics, telecom, and product recommendation work. In Germany, remote collaboration is often enough, but on-site sessions can help when data access or stakeholder reviews are sensitive.
What strong specialists do
A strong GNN specialist does more than train a model. They choose the right graph formulation, handle feature engineering, tune sampling and message passing, and explain why the model performs well or fails. They also know when a simpler baseline is better.
Deliverables
- Graph modeling and feature design
- Baseline comparison and model selection
- Training, validation, and error analysis
- Inference pipeline support
- Documentation for handover and maintenance
Frequently asked questions
What clients ask us most about Graph Neural Network — answered in short.
A Graph Neural Network is used when the data is connected and those connections carry meaning. It helps with recommendation, fraud detection, link prediction, entity matching, and graph classification. It is a better fit than plain tabular models when relationships between records matter.
No. GNN usually refers to a specific family of neural models that learn from graph structure, while graph machine learning is the broader area that also includes feature-based methods, embeddings, and classical graph algorithms. Teams often compare both before picking a solution.
A graph neural network is often compared with XGBoost, random forests, graph embeddings, and rule-based graph analytics. The right choice depends on whether the project needs deep learning on connected data or a simpler model that is easier to run and explain. Strong specialists can show that trade-off clearly.
A strong GNN specialist usually knows PyTorch or TensorFlow, graph data modeling, Python, and evaluation methods for imbalanced data. Experience with Neo4j, DGL, or PyTorch Geometric can help, but the key is understanding how to turn raw relationships into usable features and training data.
Small proof-of-concept work can often start with one experienced Graph Neural Network specialist. Production work needs someone who can handle data quality, training stability, inference design, and monitoring. If the graph is large or business-critical, you want proven experience, not just research familiarity.
Yes, most GNN work can be done remotely from Germany if the data can be accessed securely. On-site time is mainly useful for sensitive data review, stakeholder workshops, or fast alignment with product and data teams. Many projects use a mixed setup.
Look for clear baselines, careful graph construction, and evaluation that matches the business task. A strong graph neural network expert explains why a model beats a simpler approach, where it fails, and how it will be maintained. If the answer is only about the model architecture, that is not enough.
A good Graph Neural Network freelancer will ask about the graph source, node and edge types, target labels, latency needs, and how success will be measured. They should also ask whether the project is research, a pilot, or a production system. Those details change the method and the delivery plan.
The average hourly rate of freelancers in Germany who have used Graph Neural Network in their recent projects is 86 €, which corresponds to a daily rate of about 687 € 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, 77% hold at least a Master's degree, and 38% 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 1.9 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 (31%).
The most common industries among freelancers in Germany who have used Graph Neural Network in their recent projects are Information Technology (85%), Education (54%), and Automotive (38%).
The most common business areas among freelancers in Germany who have used Graph Neural Network in their recent projects are Information Technology (100%), Product Development (92%), and Research and Development (92%).
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
Get in touch with the FRATCH team and we will get back to you within 4 hours.
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Philipp Thomaschewski
FRATCH CEO
