Qdrant Experts in Germany
, matched in minutes from over 15,000 CVsHire experts who build semantic search, recommendation systems and retrieval-augmented generation with Qdrant, its APIs and vector search ecosystem. FRATCH connects you with vetted, available freelancers through fast, precise AI matching.
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
David O.
Last position:
Machine Learning Researcher at Pattern Recognition Lab, FAU
- Fine-tuned and evaluated alternative backbone architectures for the AFFGAN handwriting style encoder to better capture writer-specific features, benchmarking against the original baseline architecture and replacing the legacy VGG19 architecture with a custom Transformer-based encoder.
- Extended the system beyond word-level synthesis to line-level handwriting generation, integrating and adapting an existing line-generation architecture to support full-sentence output.
- Deployed the handwriting generation pipeline as a web application, enabling users to select a writing style, input custom text, and instantly generate a corresponding handwriting image.
- Integrated TrOCR into a teacher–student distillation framework to evaluate and improve readability of generated handwriting, improving OCR accuracy by 20% over the baseline.
- Boosted user-study pick-rates by 40%, demonstrating a significant leap in the perceptual quality and realism of the generated handwriting compared to the baseline model.
Tech: PyTorch, Python, GANs, CNN, ViT, TrOCR, Streamlit, CUDA.
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.
Hamza K.
Last position:
Academic Research Contributor in Health Sector (Volunteer) at n/a
- 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.
Eric K.
Last position:
Digital roadside assistance platform at SABK
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.
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
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
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 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
Discover over 15,000 top freelancers
Statistics of experts using Qdrant
Aggregated from the professional profiles of matched freelancers.
Experience
14 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
96%
Master's degree or higher
79%
Doctorate
18%
Certifications per freelancer
3
Most common languages
German, English, French
Speak two or more languages
97%
Based on our profile pool as of 29 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 29 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 (53%)
- Professional Services (47%)
- Automotive (44%)
- Banking and Finance (31%)
- Manufacturing (31%)
- Government and Administration (25%)
- Healthcare (22%)
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 similarity search over embeddings. It stores vectors together with payload data, then retrieves the most relevant records for a query. Teams use it to power semantic search, recommendation features, document discovery and retrieval-augmented generation.
Collections and retrieval
Qdrant organizes vectors in collections and supports payload filtering, scoring and configurable distance metrics. Its REST and gRPC APIs support application integration, while hybrid search can combine dense vectors with sparse signals. Strong implementations define useful payload schemas and retrieval rules before tuning performance.
Ecosystem and tooling
Qdrant fits into modern machine learning and application stacks. Specialists commonly connect it with embedding models, Python or TypeScript services, LangChain, LlamaIndex and orchestration tools. They may also work with Docker, Kubernetes, cloud storage, observability systems and data pipelines that keep collections current.
Typical project work
- Design collections, payload schemas and indexing strategies
- Ingest documents, products, images or event data as embeddings
- Build filtered semantic search and recommendation flows
- Connect retrieval to large language model applications
- Operate Qdrant deployments with backups and monitoring
When specialists help
Companies bring in freelance Qdrant specialists when a proof of concept must become a reliable product, when search quality is difficult to evaluate, or when an existing vector store needs migration. Germany-based teams may value on-site workshops for discovery and architecture, with implementation continuing remotely across distributed teams.
What strong experts deliver
Effective professionals understand both retrieval quality and production operations. They select embedding models carefully, design evaluation sets, inspect false matches and control latency, memory use and update workflows. They also explain trade-offs clearly and document how application teams should query and maintain the system.
Frequently asked questions
Before you brief your next project: the most common questions about Qdrant.
Qdrant is used to search high-dimensional embeddings by meaning rather than exact keywords. Companies use it for semantic document search, product recommendations, image retrieval, fraud analysis and retrieval-augmented generation.
Qdrant is focused on vector similarity and payload filtering, while Elasticsearch and OpenSearch offer broader text-search, logging and analytics capabilities. The right choice depends on whether the main requirement is embedding retrieval, conventional search, or a combined search architecture.
Qdrant can suit teams that want open-source software, deployment control and a clear vector-search data model. A managed vector service may reduce operational work, so the decision should consider hosting preferences, security requirements, scaling patterns and the skills available to maintain the system.
A strong Qdrant freelancer should understand embedding models, document chunking, Python or TypeScript APIs and data ingestion. Experience with LangChain or LlamaIndex, large language model applications, Docker, Kubernetes and observability is also valuable.
Qdrant projects need more than basic API knowledge when they support a customer-facing search or AI feature. Look for a professional who can design the data flow, evaluate retrieval quality, handle updates and explain operational trade-offs for your workload.
Qdrant work is well suited to remote collaboration because schemas, retrieval tests and deployment settings can be reviewed asynchronously. For German companies, German or English communication may matter for workshops, documentation and coordination with product, data and infrastructure teams.
Ask a Qdrant professional to explain a real retrieval design, including collection structure, payload filters, embedding choices and evaluation methods. Strong answers address irrelevant matches, changing documents, latency, backups and how search quality will be measured.
A common Qdrant mistake is treating vector search as a simple database lookup without testing embedding quality or query coverage. Other problems include poor chunking, missing metadata filters, weak update processes and no monitoring for drift or changing user needs.
The average hourly rate of freelancers in Germany who have used Qdrant in their recent projects is 95 €, which corresponds to a daily rate of about 763 € based on an 8-hour working day.
Of the freelancers in Germany who have used Qdrant in their recent projects, 96% hold at least a Bachelor's degree, 79% hold at least a Master's degree, and 18% hold a doctorate.
On average, freelancers in Germany who have used Qdrant in their recent projects have 14 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 French (6%).
The most common industries among freelancers in Germany who have used Qdrant in their recent projects are Information Technology (97%), Education (53%), and Professional Services (47%).
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 (72%).
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 
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