Neo4j Experts in Munich
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Meet FRATCH Experts in Munich, who have recently used Neo4j
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
Felix S.
Last position:
App Developer at XIXUM-Modeler
- Developing a model-based AI where natural language is interpreted as formal relations.
- Natural language terms are not considered rigid but fluid and can be negotiated in a context so meaning resolves by iteratively specifying.
- Develops all kinds of model solutions.
- Backed by natural language and data annotation.
- Requirements to code and other solutions.
Thomas H.
Last position:
Senior MLOps, DevOps Engineer at Trianel Energy
- Build and operate an end-to-end MLOps platform on Azure ML and Kubernetes (Kubeflow) for the automated deployment, monitoring, and scaling of forecasting models (including Temporal Fusion Transformer, Informer, Autoformer).
- Implement CI/CD pipelines in Azure DevOps for the full ML lifecycle – from resource provisioning (Terraform), data transformation (Hugging Face Datasets, Pandas, PyTorch, CUDA cluster) through training and evaluation to model registry and endpoint deployment.
- Integrate MLflow for experiment tracking, model versioning, performance monitoring, and automated registration in the Azure Model Registry.
- Develop and containerize 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), log centralization, and cost monitoring.
- Automate infrastructure provisioning and model deployment using Terraform, Helm, and Azure CLI; connect to existing market data systems and event pipelines.
- Migrate existing workloads and databases (IONOS → Azure, MongoDB) with integration into central MLOps workflows and internal networks.
- Extend 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.
- Analyze and architect a software solution to process large volumes of data efficiently (>3000 messages/sec.) (market data store).
- Spring Boot / Java 21 container development with RabbitMQ for distributing stock market data via 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.
- Integration of RESTHeart to create a REST API for MongoDB.
- Build 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.
- Develop Python scripts for transforming and cleaning incoming stock market data (Pandas, scikit-learn).
Omar A.
Last position:
Senior Fullstack AI Engineer (Team Lead – B2C Platform) at mama health
- Partner directly with C-level leadership (CEO, CAIO, CTO) on architecture, OKR strategy, and cross-team roadmap prioritization, translating strategic goals into structured engineering requirements.
- Surfaced and mapped technical debt across the entire organization with C-level leadership and co-defined a prioritized remediation strategy, balancing debt paydown against feature delivery.
- Led code reviews and technical standards across the team, fostering a mentor-first environment with two-way feedback dialogue — pairing on complex pipeline work and unblocking junior engineers on async architecture patterns.
- Re-architected the AI companion's core processing pipeline from synchronous to asynchronous with a queue-based worker architecture, enabling horizontal scalability and cutting upload processing time ~4x (from ~22s to 5–10s) while improving response accuracy.
- Designed an AI-driven document intelligence workflow with automatic multi-document classification, per-document summarization, and relevance guardrails for the patient care journey.
- Built a unified patient memory system (short- and long-term context) bridging the document vault and chatbot into a single bidirectional, context-aware platform.
Stephan B.
Last position:
Freelance Data Scientist at Baier Data & AI Consulting
Sabine S.
Last position:
Consulting, freelancing at Self-employed
Demand and lead gen specialist – strategist and hands-on digital marketing: email touchpoints, nurturing, scoring; PPC – Google Ads, LinkedIn
Janusz M.
Last position:
IoT Edge Computing / Self-Driving-Cars at Automotive consulting company
- Platform: Python ecosystem, RHEL 8, K10, AWS IoT Core, AWS Lambda, MLOps
- Software: Java JEE/cloud, IntelliJ IDEA, AWS IoT Core, AWS Edge and Lambda, AWS SageMaker SDK, Docker Compose, Kubernetes, OpenShift 4, Tekton, Flux, Helm charts, JSON/XML technology, Nginx, Apache Spark, OpenAI (GPT Plus, DALL-E 3, Whisper), GAN, GitHub Copilot, AI/machine and deep learning, Jupyter notebooks, TensorFlow 2, Colab, Keras API, Prometheus, Grafana, Conda, Python 3.9, PySci stack (NumPy, pandas, Scikit-learn, matplotlib)
- Responsible for webinar:
- IoT edge computing: architecture, components, resources, management
- IoT edge computing with MicroK8s, designing and creating flows/diagrams for AWS, three-step model for IoT ecosystem
- IoT processes, connectivity, data transfer and deployment, security
- Optimization of edge computing for IoT networks and services (AWS SQS queue, SNS notifications, events, analytics, buttons, device management/defender, Things Graph)
- Machine/deep learning frameworks (models, training, pipeline optimization, deployment in the cloud/at the edge (OpenShift), monitoring workloads with Prometheus and Grafana)
- Performance optimization for low latency/resilience using adaptive ML/DL/RL models for customer IoT data
- Analysis of large sensor data sets with Apache Spark, Kafka clusters
- Kasten K10 data management platform on Kubernetes multi-cluster with Helm chart, deployment, backup/disaster recovery (RTO/RPO), data lifecycle and security management
- Implementation of multilayer artificial neural network (ANN) with TensorFlow 2 and Colab for regression and classification; data analysis and provisioning for applications; development of models for testing and training, deployment of models
- Automation of business streamline processes with AI (Azure OpenAI, Discord bots/Zapier apps AI assistants (IntelliJ, GitHub Copilot))
Discover over 15,000 top freelancers
Statistics of experts using Neo4j
Aggregated from the professional profiles of matched freelancers.
Experience
22 years (Germany: 17 years)
Position duration
2.1 years
Positions per freelancer
12 (Germany: 10)
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Banking and Finance, Professional Services
Certification focus areas
Information Technology, Product Development, Finance
Bachelor's degree or higher
100% (Germany: 94%)
Master's degree or higher
83% (Germany: 69%)
Doctorate
33% (Germany: 14%)
Certifications per freelancer
2 (Germany: 3)
Most common languages
English, German, Spanish
Speak two or more languages
100% (Germany: 95%)
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 Munich 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 Munich using Neo4j
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.
Neo4j 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 (100%)
- Banking and Finance (57%)
- Professional Services (57%)
- Automotive (43%)
- Manufacturing (43%)
- Advertising (29%)
- Aerospace and Defense (29%)
- Education (29%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Graph data
Neo4j is a graph database for connected data. It stores nodes, relationships, and properties in a way that fits networks, paths, and dependencies. Companies use it for recommendation engines, fraud checks, identity graphs, knowledge graphs, and routing logic.
Cypher work
Cypher is the query language most experts use with Neo4j. Strong work includes data modeling, query design, index use, and refactoring slow traversals. In Munich, this often supports product teams, mobility projects, and data-heavy internal tools that need fast relationship queries.
Typical deliverables
- Graph data models for new or existing products
- Cypher queries for search, ranking, and path finding
- Import pipelines from SQL, APIs, or event streams
- Performance tuning for read-heavy graph workloads
- Neo4j Aura or self-managed setup guidance
Ecosystem skills
Neo4j experts often work with Java, Spring Boot, Python, JavaScript, Docker, and cloud services. They also handle import tools, driver setup, and security basics such as roles and access control. Good specialists know when to use a graph and when a simpler relational design is enough.
When to bring help
Companies bring in freelance Neo4j specialists when data links are central to the product and the in-house team needs focused support. That can mean a new graph model, a migration from another store, query fixes, or production support after a release. Remote work is common, but on-site sessions in Munich help when teams need deep modeling workshops.
What strong experts do
A strong Neo4j professional starts with the data shape, not the query. They ask how entities connect, which paths matter, and what needs to stay fast under load. They document decisions clearly, work well with product and data teams, and leave behind models that are easy to maintain.
Frequently asked questions
Everything clients usually want to know about Neo4j, in one place.
Neo4j is used when relationships matter more than flat records. Common uses include recommendations, fraud detection, network analysis, knowledge graphs, and access or dependency mapping. It is a good fit when a graph database can answer connected-data questions more naturally than tables.
Neo4j is different from relational databases because it stores relationships as first-class data. PostgreSQL or MySQL can model links, but complex traversals often become harder to read and slower to maintain. If your queries follow paths, hops, or many-to-many connections, Neo4j is often the better tool.
A strong Neo4j specialist should understand data modeling, Cypher, indexing, and performance tuning. They should also be able to explain trade-offs in plain language and connect graph design to product goals. Good work usually includes schema decisions, query reviews, import support, and cleanup of weak models.
Most Neo4j experts also know Cypher, graph modeling, and one or more general-purpose languages such as Java, Python, or JavaScript. Many also work with Docker, REST APIs, ETL flows, and cloud deployment basics. If your project touches Spring Boot or backend services, that experience can help a lot.
Simple proof-of-concept work can be handled by a generalist who knows the basics of Neo4j and Cypher. Production systems need a specialist who can design the graph well, keep queries predictable, and avoid costly modeling mistakes. The more central the graph is to your product, the more important deep expertise becomes.
Most Neo4j work can be done remotely because modeling, query work, and tuning can be reviewed online. On-site time in Munich can still help for discovery sessions, workshops, and fast decisions with product or data teams. Many projects use a mix of remote delivery and a few focused meetings.
Look for clear graph models, readable Cypher, and a track record of solving real business problems with Neo4j. Strong experts can explain why a relationship model fits, where indexes matter, and how they would test performance. Ask for examples of imports, migrations, or production tuning, not just demos.
Yes, Neo4j is often used for knowledge graphs because it represents entities and their links very naturally. It can also support search-like experiences when you need path awareness, ranking, or related-item discovery. For full-text search alone, teams often pair it with a search engine rather than use Neo4j by itself.
The average hourly rate of freelancers in Munich, Germany who have used Neo4j in their recent projects is 107 €, which corresponds to a daily rate of about 857 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Neo4j in their recent projects, 100% hold at least a Bachelor's degree, 83% hold at least a Master's degree, and 33% hold a doctorate.
On average, freelancers in Munich, Germany who have used Neo4j in their recent projects have 22 years of professional experience, with a single engagement typically lasting around 2.1 years.
The most common languages among freelancers in Munich, Germany who have used Neo4j in their recent projects are English (100%), German (86%), and Spanish (29%).
The most common industries among freelancers in Munich, Germany who have used Neo4j in their recent projects are Information Technology (100%), Banking and Finance (57%), and Professional Services (57%).
The most common business areas among freelancers in Munich, Germany who have used Neo4j in their recent projects are Information Technology (86%), Product Development (86%), and Research and Development (71%).
Main locations of FRATCH Experts, who have recently used Neo4j
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 
Berlin Request a free demo
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Philipp Thomaschewski
FRATCH CEO
