Qdrant Experts in Germany
matched in minutes from over 15,000 CVs with the power of AIHire experts who design vector search, tune collections and payload filters, and connect Qdrant to embedding pipelines, RAG systems, and semantic retrieval services. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Germany, who have recently used Qdrant
Dmitry Pankov
Last position:
Freelance Digital Marketing Analyst at Freelance
- Marketing Strategy: Lead the end-to-end analysis and evaluation of cross-channel marketing campaigns across the entire Customer Journey. My focus is identifying optimization potential and deriving clear, actionable recommendations that drive measurable business impact.
- Data Science & AI: Advanced predictive modeling (Churn, LTV), market basket analysis, clustering, and real-time AI-powered audience discovery utilizing RAG/LLMs.
- Marketing Analytics & Measurement: End-to-end attribution analysis, Marketing Mix Modeling (MMM), audience segmentation, conversion path analysis, and A/B testing across all major platforms.
- Data Engineering & Reporting: Designing and managing robust, multi-platform data pipelines (BigQuery, GCP) for data consolidation, automated dashboard generation, and critical API integrations.
Thorsten Huber
Last position:
Product Owner, AI Manager at crazyALEX.de GmbH
Digitizing real-world places with 3D/LiDAR scans to make spatial data usable for AI applications and to derive concrete use cases and prototypes from it.
- Digital capture of real-world places as a basis for faster planning and analysis
- Browser-based access to 3D data for easier use and coordination
- Turning spatial data into concrete use cases, prototypes, and AI training scenarios
- Planning basis for urban development and other digital future applications
Keywords: LiDAR, 3D scan, AI, use cases, AI training, prototyping, Python, web development, data models, architecture
Nemanja Milenković
Last position:
AI Engineer / Senior Backend Engineer at Intelycx
Manufacturing intelligence platform with enterprise workflows, RAG, real-time AI assistant features, and multi-repository backend architecture.
- Built and extended production AI/backend services with Django, DRF, FastAPI, GraphQL, Celery, PostgreSQL, MySQL, Redis, and WebSockets across a modular multi-repository platform.
- Contributed to ARIS V2, a real-time manufacturing AI assistant using LangChain, LangGraph, MCP tool orchestration, planning/execution flows, OpenAI, AWS Bedrock, Qdrant, and Elasticsearch/OpenSearch-backed retrieval.
- Supported rollout expansion from ARIS V1 in 4 of 17 client production plants to ARIS V2 currently active in 13 of 17 plants, increasing real-world deployment coverage to more than 50% of the client footprint.
- Worked on document-grounded RAG functionality including ingestion, OCR, chunking, embeddings, indexing, retrieval, reranking, and grounded answer generation for industrial workflows.
Stack: Python, Django, DRF, FastAPI, LangChain, LangGraph, GraphQL, Celery, WebSockets, OpenAI, AWS Bedrock, Qdrant, Elasticsearch/OpenSearch, PostgreSQL, MySQL, Redis, Docker.
Tezcan Dilshener
Last position:
Solution Architect / Project Manager at German Football Association
- Overall responsibility for the project lifecycle from scope definition to completion
- Close collaboration with platform teams, IT leaders, and external service providers
- Application of SAFe principles and structured sprint work
- Creation of a migration roadmap with clear milestones
- Monitoring of the lifecycle: onboarding, repository migration, replication of permissions, and system tests
- Visualization of the architecture with PlantUML and Gliffy as well as documentation in Confluence
- Regular status reports and running knowledge transfer sessions
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
Michael Löbbecke
Last position:
CTO at SNIPE Germany GmbH
Software service provider for AI solutions, automation, and custom software.
Team: built from 2 to 10 developers, 8 direct reports, partly remote · Portfolio: 6 parallel projects (€25k–€250k), scaling > €1M
- Ensured delivery capability for 6 parallel customer projects: role model, capacity planning (510–660 productive person-days/year), hiring roadmap with €380k–€440k/year personnel budget
- Established SDLC framework from scratch in under 6 months: REQ/SPEC structure, V-model gates, GitHub Issues as specification, Definition of Done, release process; consistent, auditable development process across all customer projects
- Prepared large program (3,000–4,000 person-days over 18 months) for decision readiness: AI-native industry platform for the construction sector; scoping, team profile for 8–10 developers, phase 0 budget €390k
- Designed and introduced self-hosted AI platform: vLLM, LiteLLM, Qdrant, Supabase; agent architecture, MCP integration, OCR pipelines; prepared GPU investment with break-even model (month 15–16)
- Systematized presales end to end: lead qualification with maturity scoring, discovery workshops, own sizing model, costing with loaded hourly rates, structured handover to development
- Tangibly improved the security level of a customer platform: penetration test incl. re-verification of all findings
Hamza Khan
Last position:
Academic Research Contributor in Health Sector (Volunteer)
- Acted as technical consultant to optimize multi-layer ensemble models combining ResNet, CNN-BiGRU-Attention, and XGBoost.
- Guided implementation of a Logistic Regression meta-learner to solve class imbalance problems, achieving 92.86% accuracy and 0.9644 AUC on PTB-XL and Chapman-Shaoxing datasets.
Deepak Reddy Narra
Last position:
Machine Learning Engineer at go AVA GmbH
- Designed and built a multi-tenant Python/Flask API platform with JWT + API-key authentication, scoped access control, and service-level orchestration as the backbone for AI applications.
- Built a multimodal RAG system with hybrid chunking, dense/sparse embeddings, hybrid retrieval, reranking, and vector search to deliver grounded, high-precision responses across enterprise data.
- Productionized AI workflows with Docker, CI/CD, Redis-backed async job tracking, webhook callbacks, external AI/media service integrations, and runtime health/reliability controls.
Sophia Wagner
Last position:
AI Engineer & Technical Consultant at Freelance
- Delivered ML pipelines for OCR, semantic search, and computer vision
- Integrated Azure AI Agents and GPT workflows for automation and QA
- Deployed cloud-based FastAPI services with scalable architecture
- Created integration docs and advised on LLM production readiness
Patrik Garten
Last position:
Technical Lead Conversational AI at CANCOM
- Technical lead of a team developing agentic chatbot solutions (React, TypeScript, Python, FastAPI)
- Architecture design for multi-LLM dialog systems - focus on maintainability, UX, and autonomous execution
- Stakeholder alignment, CI/CD processes, and AI integration at enterprise level
Thomas Langer
Last position:
Consultant for AI-driven process automation at Lumiz
AI-driven automation of purchasing on a printing company's website, including selecting delivery times, order options, ordering, payment, and uploading print data from the Lumiz Cloud.
Michael Dobmeier
Last position:
Sole Proprietor at Unternehmens- und Managementberatung Michael Dobmeier
- Advising and technical implementation of AI integration and workflow automation for small and mid-sized businesses
- Building a multi-tenant AI agent platform as a product base
- Combining strategic consulting, technical implementation, and team enablement
Project examples:
- Development and architecture of the SOLUMiDO Agent-UI platform for integrating digital colleagues into business processes (Next.js, TypeScript, PostgreSQL, Keycloak)
- Development of the ToolChange Assistant: a multilingual, voice-controlled AI agent for optimizing setup times and reducing errors
- Implementation of an AI email assistant for intelligent email classification and processing with Microsoft 365 integration
- AI video marketing integration (STORYNEXT) for mid-sized companies, including AI-assisted briefing and performance analysis
- Social media content automation with AI text creation, approval workflow, and automatic publishing
- Business process analysis and digital solutions implementation for facility management service providers
- Digital consulting and web presence development for associations in rural areas
Christian Weinbörner
Last position:
Interim Business Analyst / Product Owner at Bundesdruckerei GmbH (via FourEnergy GmbH)
- Initial assessment of requirements based on a business value prioritization framework
- Identification of issues as well as requirement gathering and evaluation using UML, BPMN, and design thinking methods for iterative requirements analysis through interviews and workshops
- Use of user story mapping in Miro to visualize and align functional requirements (e.g. correct transmission of all application data and attachments to the specialist system) as well as non-functional requirements (e.g. complete and verifiable deletion of an applicant's data) with stakeholders
- Proactive stakeholder management of internal and external stakeholders from public authorities, business units, organizations, and companies
- Preparation of status reports to communicate project progress and upcoming tasks transparently
- Responsibility for a REST-based integration solution (middleware) for secure data exchange between core systems and external specialist applications; ensuring stability and performance in day-to-day operations
- Support for Product Owners in prioritizing backlog items and in product discovery
- Communication of planning to internal and external stakeholders as well as interim assumption of Product Owner tasks and responsibilities during a staff change
Tino Truppel
Last position:
Director Technology at Forte Digital Germany
- Leading 20+ staff in development, site reliability engineering, and architecture.
- Leading the group-wide agentic AI initiative (Norway, Poland, Germany).
- Hands-on solution architect and AI consultant for over 50% of my working time on client projects in the publishing sector – from local publishers to international corporations.
- Strategic consulting and technical implementation of AI workflow platforms (n8n, Workato).
- Developing prototypes for traditional, AI-based, and agentic AI workflows.
Christian Worsch
Last position:
Senior Full-Stack Developer at Quantrefy GmbH
- Developed a scalable middleware to connect 5 core ESG data provider APIs using Python (FastAPI, Django, Flask)
- Performed data analysis, reporting, and forecasting of ESG data with PHP (Laravel, Symfony)
- Integrated features into an existing data analytics platform, introducing React.js (Redux) with JavaScript/TypeScript after 1.5 years of stagnation
- Implemented comprehensive test automation using pest and unittest
- Built REST APIs and microservices for the Laravel backend, incorporating GitHub Actions
- Integrated LLM and GenAI capabilities
- Managed relational databases (PostgreSQL)
- Technologies: PHP (Laravel, Symfony), Python (FastAPI, Pandas, NumPy), AWS, Node.js (Fastify), AngularJS, TypeScript, Docker, Kubernetes (OpenShift)
Discover over 15,000 top freelancers
Statistics of experts using Qdrant
Aggregated from the professional profiles of matched freelancers.
Experience
15 years
Position duration
3.1 years
Positions per freelancer
10
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Automotive, Education
Certification focus areas
Information Technology, Business Intelligence, Human Resources
Bachelor's degree or higher
91%
Master's degree or higher
73%
Doctorate
23%
Certifications per freelancer
2
Most common languages
German, English, Hindi
Speak two or more languages
96%
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 Qdrant
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
Qdrant is a vector database for semantic search and retrieval. It stores embeddings and finds similar items by distance, then adds metadata filters for precise results. Teams use it for recommendation, duplicate detection, image or text search, and retrieval-augmented generation.
What experts deliver
- Collection design and schema choices
- Payload filtering and ranking logic
- Ingestion pipelines for embeddings
- Search endpoints for apps and services
- Tuning for relevance, latency, and scale
A strong specialist understands how vectors, metadata, and query patterns work together. They keep the search path simple, readable, and stable under real traffic.
Ecosystem
Qdrant often sits beside embedding models, Python services, TypeScript apps, and LLM workflows. It is commonly used with OpenAI or local embedding models, LangChain, LlamaIndex, and container-based deployment stacks. Good experts know how to wire these pieces together without turning the system into a black box.
When companies bring help
Companies usually bring in freelance expertise when a search prototype has to become a production service. The same applies when relevance is weak, ingestion is slow, or filters are too broad. In Germany, this often means working with product teams, data teams, and backend specialists across English and German communication.
Strong delivery
- Clear understanding of distance metrics and vector size
- Practical approach to payload modeling
- Careful indexing and update behavior
- Monitoring for search quality and service health
- Solid API design and error handling
The best professionals make Qdrant easy to operate. They document query logic, explain trade-offs, and leave teams with a system that is maintainable after launch.
Typical projects
Qdrant specialists are often hired for semantic knowledge search, product matching, internal assistants, recommendation features, and content discovery. They also help migrate from older search setups when vector search becomes a core requirement. For Germany-based companies, remote work is common, but on-site sessions help when search logic must be aligned with local product owners and domain experts.
Frequently asked questions
Before you brief your next project: the most common questions about Qdrant.
Qdrant is used for vector search, semantic retrieval, and recommendation features. Teams store embeddings in it and combine similarity search with metadata filters to get results that are both relevant and controllable. It is a strong fit for RAG, duplicate detection, and content discovery.
Qdrant is built specifically for vector data, so it is usually the better choice when similarity search is the main problem. Elasticsearch can work well when text search and vector search must live together, while PostgreSQL vector extensions fit simpler setups or existing database-first stacks. A good specialist chooses based on the search pattern, not the brand name.
Most people search for Qdrant by its product name, and some refer to it as a vector database or vector search engine. In practice, searchers also compare it with “Qdrant DB” or simply “Qdrant vector database” in technical discussions. A strong specialist understands all of these terms and the same underlying use case.
A strong Qdrant specialist usually understands embeddings, retrieval design, API integration, and query filtering. Helpful adjacent skills include Python, backend services, containers, and working with LangChain or LlamaIndex. For production work, monitoring and data modeling matter just as much as the database call itself.
Qdrant projects benefit from freelance help as soon as relevance, scale, or integration decisions matter. If the team only needs a quick proof of concept, broad software experience may be enough. If the system will power search in production, look for someone who has already tuned collections, filters, and ingestion flows.
Yes, Qdrant work is usually well suited to remote collaboration. Most tasks are code, schema, and search-quality focused, so the specialist can work with German teams through shared tickets, reviews, and clear demos. On-site sessions can still help during discovery or when product language and domain terms need fast alignment.
Look for practical evidence, not general claims. A strong Qdrant freelancer can explain why they chose a distance metric, how they modeled payloads, and how they tested search quality with real examples. They should also be able to discuss trade-offs between speed, relevance, and maintainability.
Qdrant is not the right first choice when the problem is mostly keyword search, relational reporting, or transaction-heavy storage. It works best when semantic similarity is central and the app needs fast retrieval over embeddings. A good specialist will say that clearly and suggest a better fit if needed.
The average hourly rate of freelancers in Germany who have used Qdrant in their recent projects is 99 €, which corresponds to a daily rate of about 790 € based on an 8-hour working day.
Of the freelancers in Germany who have used Qdrant in their recent projects, 91% hold at least a Bachelor's degree, 73% hold at least a Master's degree, and 23% hold a doctorate.
On average, freelancers in Germany who have used Qdrant in their recent projects have 15 years of professional experience, with a single engagement typically lasting around 3.1 years.
The most common languages among freelancers in Germany who have used Qdrant in their recent projects are German (100%), English (96%), and Hindi (8%).
The most common industries among freelancers in Germany who have used Qdrant in their recent projects are Information Technology (96%), Automotive (54%), and Education (50%).
The most common business areas among freelancers in Germany who have used Qdrant in their recent projects are Information Technology (100%), Product Development (100%), and Research and Development (77%).
Main locations of FRATCH Experts, who have recently used Qdrant
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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We always have the time for a call or email!
Philipp Thomaschewski
FRATCH CEO
