Milvus Experts in Germany
matched in minutes from over 15,000 CVs with the power of AIHire experts who design Milvus search stacks, tune vector indexes, and connect embedding pipelines to production apps. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Germany, who have recently used Milvus
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
Rutger Boels
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 Ghafarlangroudi
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 Anwar
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 Komorowsky
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 Schönherr
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
2.5 years
Positions per freelancer
8
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Education, Automotive
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 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 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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
Vector search core
Milvus is an open source vector database used to store and search embeddings at scale. Teams use it for semantic search, recommendation, image similarity, and retrieval for LLM apps. It fits systems that need fast nearest-neighbor search over high-dimensional vectors.
What specialists deliver
- Build collection schemas and indexing strategies
- Connect embedding models to search and retrieval flows
- Tune query latency, recall, and memory use
- Set up filtering, partitioning, and hybrid search
Ecosystem fit
Milvus is often used with Python, LangChain, LlamaIndex, Docker, Kubernetes, and cloud object storage. Strong specialists understand how embeddings are produced, how metadata is stored, and how clients query the service from product code or data pipelines. They also know when to use Milvus versus a simpler vector store.
When companies bring help
Companies bring in freelance Milvus experts when a prototype must become production-ready, when search quality is unstable, or when ingestion starts lagging behind traffic. In Germany, this often comes up for B2B software, industrial data search, and multilingual product discovery. Remote work is common, but on-site workshops help when teams need architecture decisions and knowledge transfer.
What strong experts know
A good Milvus specialist can explain why a certain index was chosen, how embeddings are generated, and how metadata filters affect results. They watch schema design, query patterns, observability, and rollback plans. They also know the limits of vector search and how to combine it with keyword search or business rules.
Common project outcomes
Milvus work usually ends with a stable search service, a working retrieval layer for LLM features, or a recommendation backend that product teams can keep extending. Deliverables often include setup guides, test queries, migration notes, and clear handover steps for the next specialist.
Frequently asked questions
Quick answers to the questions that come up most around Milvus.
Milvus is used for vector search over embeddings, so teams can find similar text, images, audio, or product data by meaning rather than exact keywords. It is common in semantic search, recommendation, and retrieval workflows for LLM applications. Companies usually bring it in when search needs to feel smarter and respond quickly.
Milvus handles similarity search over vectors, while a classic search engine is built around terms, fields, and ranking rules. Many teams combine Milvus with keyword search instead of replacing it. That gives them semantic matching plus exact filters and business logic.
Milvus is a strong choice when the search layer must grow, stay flexible, and support more than a simple demo. It is often weighed against Pinecone, Weaviate, or pgvector. A good specialist helps choose based on query patterns, operations, and the rest of the stack.
A strong Milvus expert usually knows embeddings, Python, API design, and data pipelines. Experience with Docker or Kubernetes helps when the service runs in a controlled environment. If the project involves RAG, the specialist should also understand prompt flows, chunking, and retrieval quality.
A small proof of concept may need only one experienced Milvus specialist, but production work often needs broader system knowledge. The hardest parts are schema design, index choice, and making retrieval stable under load. If the search layer is central to the product, choose someone who has already shipped it.
Yes, Milvus work is often done remotely, especially for schema design, embeddings, and integration tasks. For German teams, clear documentation and short feedback loops matter more than location. On-site sessions can still help when the project needs architecture alignment or handover with local stakeholders.
Look for clear answers about index types, filters, recall tradeoffs, and how query latency is measured. A solid Milvus professional can explain why a design fits your data instead of repeating generic best practices. Ask for examples of production retrieval work, not just proof-of-concept demos.
The most common issues in Milvus projects are weak embeddings, poor chunking, bad schema choices, and unclear search goals. Another frequent problem is treating vector search as a full solution when keyword search or reranking is still needed. A good specialist helps separate product needs from technical limits.
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 2.5 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 Automotive (33%).
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
Get in touch with the FRATCH team and we will get back to you within 4 hours.
Would you rather directly get in touch?
We always have the time for a call or email!
Philipp Thomaschewski
FRATCH CEO
