Qdrant Experts in Germany
, matched in minutes from over 15,000 CVs with the power of AIHire experts who design vector search, semantic retrieval and recommendation systems with Qdrant, including data pipelines, embedding models and production APIs. FRATCH connects you with vetted, available freelancers whose skills match your requirements quickly and precisely.
Meet FRATCH Experts in Germany, who have recently used Qdrant
Patrick L.
Last position:
Senior GenAI Fullstack Developer at SBH (Schulbau Hamburg)
Remote freelance role focused on Agentic AI strategy, secure application patterns, and reusable agentic workflows for a government agency.
- Development and implementation of an open-source Agentic AI strategy for a government agency, with a focus on GDPR, security, and self-hosted solutions
- Development of reusable agentic workflows and business applications that enable non-technical employees to solve business problems independently
- Implementation of nine business applications with Single Sign-On (SSO) and Azure PostgreSQL integration on Hetzner Linux servers
Techstack: Python, Streamlit, Anthropic SDK (Claude), Azure, Linux, PostgreSQL, MS SQL, Angular
Thorsten H.
Last position:
Product Owner, Software Developer, AI Manager, Technical Consultant at crazyALEX.de GmbH
AI-supported document processing and inventory management integration
Design and development of an AI-supported application for the automated processing of delivery and invoice documents, connected to SelectLine ERP. Documents are analyzed using AI, matched with orders and line items, and prepared for posting goods receipts.
IMPACT:
- Automated extraction of structured order, delivery and invoice data from PDF and image documents
- Automatic and manual mapping of documents to orders and order line items
- Integration with SelectLine ERP for order import, status synchronization and goods receipt postings
- Traceable processing through separate analysis, mapping and posting processes as well as technical logging
- Development of a containerized end-to-end architecture with AI analysis, workflow automation and relational data storage
KEYWORDS: AI, document analysis, OpenAI, n8n, SelectLine ERP, FastAPI, Python, JavaScript, MariaDB, Docker, REST API, PDF, OCR, mapping, inventory management, goods receipt, workflow automation
Dmitry P.
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.
Ali A.
Last position:
Founder & Architect at Independent AI R&D
- Fully on-premises LLM document-examination platform for a compliance-critical banking domain: agentic LangGraph pipeline with deterministic verification, every AI judgment structured and source-anchored; ~960 automated tests, zero data egress
- GPU throughput engineering (quantized serving, speculative decoding, prefix caching): 9.5x extraction speed-up, 500+ multi-document case files per day on a single A100
- AI-native EDI/EDIFACT integration platform (~116k LOC Java 25 / Spring Boot 4, 1,900+ tests): LLM-drafted partner mappings machine-verified before go-live (DFDL conformance, field-coverage checks, dry runs), ~99.5% byte match on real customer files — replacing weeks of manual mapping per partner
Shanna T.
Last position:
Freelance Data Scientist & AI Developer at tellaev.de
- Portfolio development & customer acquisition
- Portfolio development (RAG, NLP fine-tuning, process automation with n8n) and active customer acquisition
- Positioning: GDPR-compliant, locally hosted AI solutions for SMEs
Nemanja M.
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 D.
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 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
Michael L.
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
Deepak R.
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.
Sebastian B.
Last position:
Product & AI Consultant
As a freelance employee / independent consultant, I supported a wide range of development projects and advised clients on digital innovation initiatives. A common role was acting as project manager / product owner at the interface between the client and the development team.
- Product Consulting — (Fractional) Product Owner, Freelance Product Management
- AI Services — AI consulting, training, vibe coding / agentic engineering, AI agents
- Tech Services — full-stack web development, design/UX, MVP delivery.
Thomas L.
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.
Patrik G.
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
Michael D.
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 W.
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
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 years
Positions per freelancer
9
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Education, Professional Services
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
93%
Master's degree or higher
70%
Doctorate
19%
Certifications per freelancer
2
Most common languages
German, English, Hindi
Speak two or more languages
97%
Based on our profile pool as of 9 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Germany are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows the share of experts charging 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 9 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Qdrant 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 (97%)
- Education (52%)
- Professional Services (52%)
- Automotive (48%)
- Banking and Finance (32%)
- Manufacturing (29%)
- Government and Administration (29%)
- Retail (23%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Vector search foundation
Qdrant is an open-source vector database built for storing, indexing and searching high-dimensional embeddings. It supports similarity search for text, images, audio and structured records, helping applications retrieve content by meaning rather than exact keywords. Teams use it for semantic search, retrieval-augmented generation and personalized recommendations.
Collections and payloads
Qdrant stores vectors in collections together with payload data such as document types, permissions, languages or product attributes. Payload filters can narrow searches before or after similarity scoring, which is important when results must respect business rules. Specialists work with distance metrics, collection design, indexes, snapshots and quantization to balance relevance, speed and infrastructure cost.
AI application stack
Qdrant commonly sits beside embedding services, language models and application APIs. Professionals connect it with Python or Rust clients, REST or gRPC interfaces, LangChain, LlamaIndex and custom ingestion services. They also shape chunking, metadata, embedding updates, reranking and evaluation so the complete retrieval pipeline remains consistent as data changes.
Typical delivery work
- Create semantic search for knowledge bases, support portals or product catalogs
- Add retrieval-augmented generation to an existing language-model application
- Build recommendation and similarity features for content or commerce systems
- Migrate vector workloads from another database or hosted search service
- Tune filtering, indexing and query behavior for production traffic
Qdrant can run as a self-hosted service, in containers or through Qdrant Cloud. Its Rust foundation suits teams that need a focused, API-driven vector service while keeping deployment choices open.
When expertise matters
Companies bring in freelance Qdrant specialists when a prototype must become a dependable production service, when search quality is difficult to explain, or when an existing pipeline returns irrelevant results. A professional can trace problems across source data, chunking, embeddings, filters and ranking instead of treating the vector database in isolation. For teams in Germany, remote delivery can work well when interfaces, documentation and review routines are clear; on-site collaboration may help during data and architecture workshops.
What strong specialists bring
Strong Qdrant professionals understand information retrieval as well as application engineering. They define relevance tests, inspect payload quality, monitor latency and establish safe reindexing and backup processes. They can explain why a chosen embedding model, distance metric or filter affects results, and they document operational decisions for the wider team. Experience with cloud infrastructure, observability, security and German or English-language data workflows is valuable when Qdrant supports business-critical applications.
Frequently asked questions
Before you brief your next project: the most common questions about Qdrant.
Qdrant is used to store and search vector embeddings for semantic search, recommendations, image similarity and retrieval-augmented generation. It can combine similarity search with payload filters, making it useful when results must reflect permissions, categories or other business rules.
Qdrant is focused on vector search and the operational needs around embeddings. Elasticsearch and OpenSearch offer broader text-search, analytics and document capabilities, so the right choice depends on whether vector retrieval is the core requirement or one part of a wider search platform.
A company may choose Qdrant when vector retrieval is central and needs dedicated collection management, filtering, indexing and tuning. A relational database can be simpler when vectors are closely tied to transactional records and search volume or retrieval complexity remains modest.
A strong Qdrant specialist usually understands embedding models, chunking, reranking and retrieval evaluation. Useful adjacent skills include Python or Rust, API design, LangChain or LlamaIndex, container deployment, observability and data protection.
The required experience depends on the scope. A simple semantic-search feature may need an expert who can configure collections and connect an embedding pipeline, while a production retrieval system calls for deeper knowledge of relevance testing, scaling, backups, security and failure handling.
Yes. Qdrant work is well suited to remote collaboration because configuration, code review, query testing and infrastructure changes can be handled online. On-site sessions can still be useful for workshops involving data ownership, search behavior and application architecture.
Qdrant Cloud reduces operational work around hosting and managed service administration. Self-hosting may suit teams with established infrastructure, strict deployment controls or specialized network requirements; the decision should account for security, resilience, observability and internal ownership.
Ask how the Qdrant specialist measures retrieval quality, handles changing embeddings and diagnoses poor results. Strong professionals discuss representative test queries, payload filtering, index behavior, monitoring and rollback plans rather than focusing only on initial setup.
The average hourly rate of freelancers in Germany who have used Qdrant in their recent projects is 96 €, which corresponds to a daily rate of about 764 € based on an 8-hour working day.
Of the freelancers in Germany who have used Qdrant in their recent projects, 93% hold at least a Bachelor's degree, 70% hold at least a Master's degree, and 19% 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 years.
The most common languages among freelancers in Germany who have used Qdrant in their recent projects are German (100%), English (97%), and Hindi (6%).
The most common industries among freelancers in Germany who have used Qdrant in their recent projects are Information Technology (97%), Education (52%), and Professional Services (52%).
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 (74%).
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
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FRATCH CEO
