Milvus Experts in Germany
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Meet FRATCH Experts in Germany, who have recently used Milvus
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
Rutger B.
Last position:
Partner & Managing Director at AI.IMPACT
- Building an AI & Data Consultancy Practice with the goal of helping European companies adopt Artificial Intelligence and modern data platforms
- End-to-end further development of a production system using modified coding agents (OpenCode). Tech stack: Kubernetes, Argo, Keycloak, Typescript, Grafana, GitOps, DevOps, Playwright
- Internal research project on the use of coding agents in the field of mathematical logic for creating formal models. Use of Cursor IDE and Codex, Codex CLI. Architecture design, quality control and refactoring, as well as writing code and tests. Repository (open source) available pre-launch
- Research on the role of mathematical logic as a formal language that connects IT and AI with business processes
- Project lead for collecting and deploying parking recommendations for rail vehicles with significant savings potential based on real-time data in a mobility and transport company
- Project lead for collecting and distributing process measurement points for real-time control in a mobility and transport company
- Deputy application owner for an app used for communication in the dispatching and provision of rail vehicles
Meisam G.
Last position:
Senior AI Engineer / Data Scientist at Geeks Ltd (WordUp)
Geeks Ltd is a UK-based technology company; WordUp is its AI-driven language-learning product focused on personalized vocabulary learning and intelligent educational experiences.
- Coordinate AI product delivery across Product, Engineering, Data, Operations, and leadership, translating user needs into scoped initiatives, sequencing work, surfacing blockers, facilitating hand-offs, and communicating progress.
- Own search, recommendation, retrieval, and content-enrichment features end to end, from requirements and architecture through Python/FastAPI implementation, testing, deployment, monitoring, and rapid iteration.
- Developed low-latency retrieval, ranking, and personalization services using AWS, OpenSearch, DynamoDB, embeddings, and reusable APIs, achieving <1s latency, 22% higher engagement, and 12% higher premium conversion.
- Use AI coding assistants for codebase analysis, scaffolding, refactoring, tests, debugging, and documentation while reviewing every output for correctness, architectural fit, security, maintainability, and user value.
- Represent technical work in planning and stakeholder discussions, gather requirements first-hand, challenge priorities constructively, explain delivery trade-offs, and help teammates make outcome-focused decisions.
Awais A.
Last position:
Senior Java Full Stack Developer at Media Consult Maier + Partner GmbH
- Built AI chatbots with Langchain4j/OpenAI: deployed RAG + function calling using GPT-4 Turbo LLM for medical Q&A enabling context-aware conversation for 5K+ users.
- Boosted LLM accuracy by 40% using RAG with Milvus vector DB, reducing hallucination in medical chatbots response.
- Built asynchronous functions in Liferay modules to track and analyze external site traffic and application submissions; optimized database performance through API-driven enhancements.
- Customized and maintained Liferay platform using Java (backend) and JavaScript, HTML, and CSS (frontend); generated custom themes, APIs, and modules to enhance functionality and user experience.
- Led the Liferay upgrade from 6.2 to 7.4, managing database migration and UI/UX modernization with Velocity, FreeMarker, JavaScript, and CSS; reduced page load time by 30% post-migration using caching.
- Utilized Liferay Service Builder with Hibernate to design and optimize databases, ensuring seamless data integration and robust backend architecture.
- Constructed RESTful APIs for job advertisement resources, enabled asynchronous frontend-backend communication, and imported JSON data into Liferay using Jsoup parsing techniques.
- Scheduled and automated data processing, system optimization, and file handling in Liferay Document Library; created interactive visualizations with D3.js for data insights.
- Managed OSGi builds and integrated external libraries like Apache POI and Langchain4j for scalability; handled server maintenance, portal installations, and platform optimization to ensure system reliability.
- Automated PDF and Excel processing using Apache POI for structured data storage; engineered web crawlers to extract and produced PDF files using ItextPdf to integrate job ads from external portals into Liferay’s database.
Robert K.
Last position:
IT Consultant at cloud37 Germany GmbH
- Performing IT consulting projects in Data Science and Data Management for different clients in Germany and Switzerland
- Analyzing a large number of sustainability reports using RAG (Retrieval Augmented Generation), Milvus vector databases, and Large Language Models (LLMs), provided via Watsonx.ai
- Testing prompts, LLM model types, and parameters for response quality and to avoid hallucinations
- Deploying analysis scripts to the cloud using Docker
- Programming a Streamlit app to generate responses in a user-friendly browser interface
- Using AI language agents to search company information online to pre-classify sustainability reports, e.g. by industry and number of employees
- Extending Python modules to transform, store, and import social security data into an online database system
- Mapping table structures using Python classes (column names, data types, field lengths, foreign keys, unique constraints, references to other tables)
- Automatically extracting data from Excel sheets, generating JSON files for temporary storage in a file system, and importing JSON data into DB2 database environments using batch files
- Logging SQL merge queries using the Python SQLAlchemy package for reuse across different database schemas
- Training and optimizing machine learning models in Azure Databricks to predict whiteness values measured during washing experiments with a stain monitor
- Creating charts and visualizing metrics to measure prediction quality for regression algorithms (including neural networks and random forests)
- Explaining predictions using LIME and SHAP values
Sara S.
Last position:
Senior Software Developer with a Focus on UI/UX at vGen GmbH
Development of an interactive prototype for the concept of an AI-supported Enterprise Architecture Management tool. The goal was to present complex relationships between IT systems, business processes, and departments in a way that is easy to understand and to support decision-making in the context of IT transformations.
The prototype combined data-driven analyses with guided questions and interactive visualizations. A central part was the integration of a RAG process to provide domain-specific EAM knowledge in context. The focus was on quick idea validation, user-centered interaction design, and the technical feasibility of a scalable overall concept.
Design, implementation, and validation of a RAG process for domain-specific EAM knowledge with Python, LangChain, and graph/vector databases (Neo4J, Milvus)
Business and technical requirements analysis as well as definition of an MVP
Development of the architecture and technology concept using Angular, Spring Boot, GraphQL, and Kubernetes
Design of an interaction concept and creation of a brand style guide with Figma
Development of interactive prototypes with Angular, Konva.js, and TypeScript
Integration of CI/CD processes with GitLab CI/CD
Discover over 15,000 top freelancers
Statistics of experts using Milvus
Aggregated from the professional profiles of matched freelancers.
Experience
13 years
Position duration
2.3 years
Positions per freelancer
8
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Education, Professional Services
Bachelor's degree or higher
100%
Master's degree or higher
100%
Doctorate
33%
Certifications per freelancer
1
Most common languages
German, English, French
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 Milvus
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.
Milvus 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%)
- Education (67%)
- Professional Services (50%)
- Automotive (33%)
- Banking and Finance (33%)
- Transportation (33%)
- Advertising (17%)
- Aerospace and Defense (17%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Vector search core
Milvus is an open-source vector database for fast similarity search. Companies use it to power semantic search, recommendation systems, image retrieval, and RAG applications that need to find meaning, not just exact text. It stores embeddings and returns the closest matches with low latency.
What experts deliver
- Schema design for vectors, metadata, and filters
- Collection setup, indexing, and query tuning
- Integration with LLM, OCR, and search pipelines
- Production monitoring and release support
Strong specialists know when Milvus is the right fit and when a simpler search stack is enough. They also understand how retrieval quality depends on the embedding model, index choice, and data hygiene.
Ecosystem and tools
Milvus often sits with Python, Go, and Java services, plus embedding models from common ML stacks. Many teams also work with Zilliz Cloud, which is the managed Milvus offering, and connect it to LangChain, LlamaIndex, or custom APIs. The best professionals keep the search layer clean and easy to operate.
When companies need help
Companies bring in freelance expertise when they start a new retrieval project, migrate from a prototype, or fix poor search relevance. In Germany, this often happens in product teams that need English and German content to work well across a shared search layer. Remote work is common, but on-site sessions help when data access or architecture reviews are sensitive.
What strong specialists know
Milvus work is not only about loading vectors. Strong professionals can explain index trade-offs, choose filters that keep search accurate, and plan for refreshes, deletion, and scaling. They also write clear handover notes so internal teams can maintain the system after launch.
Common use cases
- Semantic search across documents, tickets, and knowledge bases
- Product and content recommendations
- Image, audio, and multimodal similarity search
- Retrieval for chat and assistant workflows
These projects often fail when embeddings are inconsistent or the data model is rushed. Experienced Milvus specialists shape the pipeline so search stays useful after the first release.
Frequently asked questions
Quick answers to the questions that come up most around Milvus.
Milvus is used for vector search, which means finding items by similarity instead of exact text matches. Companies use it for semantic search, recommendations, document retrieval, and retrieval layers behind chat assistants. It is a good fit when embeddings are part of the product.
Milvus is built for large-scale vector retrieval and fast nearest-neighbor search. PostgreSQL vector search can work well for smaller or simpler setups, while Elasticsearch is stronger for keyword search and filtering. Many teams use Milvus when vector search becomes a core system, not just an add-on.
A strong Milvus specialist should know embeddings, indexing, filtering, and query tuning. They should also understand the surrounding stack, such as Python services, API design, and evaluation of retrieval quality. Experience with Zilliz Cloud, LangChain, or LlamaIndex is often useful.
A small proof of concept can be handled by one capable Milvus professional who knows vector search basics. Production work usually needs someone who has dealt with data modeling, latency, and operational stability. The more complex the retrieval logic, the more valuable deep hands-on experience becomes.
Yes. Milvus work is often remote because most tasks can be handled through code, architecture reviews, and shared test data. On-site time can still help during discovery workshops, especially when teams need alignment across product, data, and search requirements in Germany.
Not exactly. Milvus is the open-source vector database, while Zilliz Cloud is the managed service built around it. Some freelancers know both well, which helps if a team wants to start self-managed and later move to a hosted setup.
Look for clear decisions, not just a working demo. A good Milvus freelancer can explain index choices, filtering strategy, recall trade-offs, and how they tested retrieval quality. They should also leave clean code and practical notes your team can maintain.
Milvus projects often include embedding models, Python, FastAPI, Docker, and cloud services. In search and RAG setups, you may also see LangChain, LlamaIndex, OCR tools, and metadata stores. The best specialist can connect these parts without making the system hard to run.
The average hourly rate of freelancers in Germany who have used Milvus in their recent projects is 109 €, which corresponds to a daily rate of about 871 € based on an 8-hour working day.
Of the freelancers in Germany who have used Milvus in their recent projects, 100% hold at least a Bachelor's degree, 100% hold at least a Master's degree, and 33% hold a doctorate.
On average, freelancers in Germany who have used Milvus in their recent projects have 13 years of professional experience, with a single engagement typically lasting around 2.3 years.
The most common languages among freelancers in Germany who have used Milvus in their recent projects are German (100%), English (100%), and French (33%).
The most common industries among freelancers in Germany who have used Milvus in their recent projects are Information Technology (100%), Education (67%), and Professional Services (50%).
The most common business areas among freelancers in Germany who have used Milvus in their recent projects are Information Technology (100%), Product Development (100%), and Business Intelligence (83%).
Main locations of FRATCH Experts, who have recently used Milvus
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
