Qwen Experts in Germany
in minutes from 15,000 CVs with the power of AIHire experts who work with Qwen, Tongyi Qianwen and the wider Alibaba Qwen model family. They handle chat assistants, retrieval-augmented systems, custom prompting, evaluation and deployment workflows. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Germany, who have recently used Qwen
Dirk Peter
Last position:
Freelance Cyber Defense Lead & KRITIS/NIS2 Consultant | AI Security Architect at Self-Employed
Situation: Increasing demand for privacy-compliant AI solutions for clients in the KRITIS and mid-market sector that need to analyze sensitive media content (audio, video, documents) without sending data to public cloud LLMs.
Task: Design, deployment, and secure operation of a fully self-hosted AI infrastructure including a custom-built digital management platform for automated media analysis.
Action: Architected and implemented a multi-tier platform on hardened Proxmox infrastructure with frontend (Nuxt 3, Vue 3, TypeScript, Tailwind 4), backend (Laravel 13, PHP 8.4, Sanctum), data storage (PostgreSQL 16, MongoDB 7), caching/queuing (Redis 7, Laravel Queue), AI workers (Python 3.11, Whisper, DeepFace, Librosa), scheduling (Laravel Scheduler/Cron), and local LLMs (Gemma, DeepSeek, Qwen, Mistral, LLaMA, Phi) via OpenWebUI with segmented network access, API hardening, and audit logging following BSI recommendations.
Result: Fully GDPR-compliant, on-premises AI platform with zero data leakage to third parties.
Task: Overall responsibility as an external Head of Cyber Security / CISO-as-a-Service for the design, implementation, and continuous improvement of ISMS according to ISO 27001, BSI IT-Grundschutz, and NIS2.
Action: Built and managed Cyber Defense Centers (CDC) with SOC operations, integrated SIEM solutions (Splunk, Graylog), established risk-based vulnerability management (Qualys, Nessus, OpenVAS), and conducted regular infrastructure, application, and physical penetration tests.
Result: Audit-ready ISMS for multiple clients and a 60% reduction in critical vulnerabilities within 90 days.
Task: Design and execution of NIS2 assessments and operational roll-out plans for KRITIS operators.
Action: Developed an online assessment tool for automated identification of individual weakness profiles, implemented ISMS optimizations, penetration testing, awareness programs, GRC suite deployment, and delivered C-level presentations.
Result: Accelerated the consulting process by 50% and successfully prepared multiple clients for NIS2 compliance.
Task: Incident commander for crisis response, forensics, and business recovery in ransomware attacks and APT campaigns.
Action: Coordinated with state and federal police (LKA, BKA), performed forensic analysis (OSForensics, Wireshark, Kali Linux), executed disaster recovery and BCM strategies, and developed BTC extortion response strategies.
Result: 100% recovery rate within defined RTO windows and sustainable post-incident security architectures.
Action: Planned, built, and operated a hardened multi-VM infrastructure (Proxmox, 15+ VMs) with web and mail servers, Graylog, OPNsense firewalls, CRM/ERP and LLM instances, network segmentation, DDoS mitigation, automated patch management, and backup strategies.
Result: >99.5% uptime over 20+ years and zero compromises.
Action: Designed coordinated phishing campaigns with five levels of difficulty, developed e-trainings and webinars in a PDCA cycle, and led red and blue teams.
Result: Phishing click rate reduced from 35% to under 5% within three campaign cycles.
Thomas Hoefkens
Last position:
Senior MLOps, DevOps Engineer at Trianel Energy
- Build and operate an end-to-end MLOps platform on Azure ML and Kubernetes (Kubeflow) for the automated deployment, monitoring, and scaling of forecasting models (including Temporal Fusion Transformer, Informer, Autoformer).
- Implement CI/CD pipelines in Azure DevOps for the full ML lifecycle – from resource provisioning (Terraform), data transformation (Hugging Face Datasets, Pandas, PyTorch, CUDA cluster) through training and evaluation to model registry and endpoint deployment.
- Integrate MLflow for experiment tracking, model versioning, performance monitoring, and automated registration in the Azure Model Registry.
- Develop and containerize PyTorch training jobs (Azure Notebook, Jupyter Notebooks) for price and time series forecasting (PFC models) with automatic rollout via Azure ML Endpoints and REST/gRPC interfaces, Docker containerization, secured with OAuth 2.0.
- Set up monitoring and alerting mechanisms (Prometheus, MLflow Metrics), log centralization, and cost monitoring.
- Automate infrastructure provisioning and model deployment using Terraform, Helm, and Azure CLI; connect to existing market data systems and event pipelines.
- Migrate existing workloads and databases (IONOS → Azure, MongoDB) with integration into central MLOps workflows and internal networks.
- Extend the platform with LLM-based tools (LangChain, LangServe) to integrate GPT-based analysis modules into existing Spring Boot services for market anomaly detection and automated reports.
- Analyze and architect a software solution to process large volumes of data efficiently (>3000 messages/sec.) (market data store).
- Spring Boot / Java 21 container development with RabbitMQ for distributing stock market data via MongoDB (Kubernetes) with fast storage of data in Redis RMaps, deduplication, forwarding messages to Read Model queues, and building Read Models for UI display in MongoDB.
- Integration of RESTHeart to create a REST API for MongoDB.
- Build an Angular frontend to simplify data queries and master data maintenance.
- Agentic coding with remote and local LLMs (Claude Sonnet, Ollama Qwen) and MCP servers.
- Develop Python scripts for transforming and cleaning incoming stock market data (Pandas, scikit-learn).
Sundeep Kumar
Last position:
AI Engineer at Kingstech Services Pte Ltd
- Fine-tuned and deployed Generative AI and LLM models (OpenAI, DeepSeek, Qwen-2.5) using PyTorch and Hugging Face, increasing ERP automation accuracy by 25%.
- Designed and implemented a secure RAG-powered AI Chabot for customer-specific invoice and quotation generation, cutting response times by 40%.
- Architected cloud-native AI/ML pipelines on AWS and GCP with Docker and Kubernetes for scalable model training, deployment and monitoring.
- Developed and integrated an API-driven AI Chabot (Telegram) with ERP systems, boosting document processing speed by 30%.
- Built AI agents for chatbots to enable multi-step reasoning, intelligent task execution, and context-aware interactions.
- Applied ML and NLP techniques for intelligent document understanding, workflow automation, and data-driven business decisions.
Michael Schiffer
Last position:
Principal Consultant / Managing Director at The Implementers GmbH
Consulting and interim management in the automotive industry, healthcare and other sectors focusing on program management, production launches, plant management, supplier development, cost reduction, process optimization, AI implementation and AI-driven process automation.
Key projects:
Concept for performance documentation of a pain therapy department with interdisciplinary input and reconciliation with OPS catalogue (Feb 2026).
Complete billing system for a medical practice with multi-tenant support, state machine workflow, ZUGFeRD/EN16931-compliant invoice generation, OpenEMR integration; implemented with ~4 200 LOC Python, ~1 700 LOC SQL, 30+ endpoints, 13 DB migrations (Dec 2025 — Feb 2026).
Clinical decision support system for inpatient pain therapy including automated processing of pain questionnaires, AI-generated therapy recommendations and longitudinal analysis; stack: FastAPI, Ollama, PyMuPDF, React + TypeScript, PostgreSQL, Docker (Sep 2025 — Mar 2026).
End-to-end AI-powered transcription and documentation pipeline from physician-patient conversations with WhisperX transcription, five-stage LLM pipeline, review UI and self-hosted infrastructure; stack: FastAPI, WhisperX, Ollama, React + TypeScript, Docker (Aug 2025 — Feb 2026).
AI-powered pipeline for document recognition and automated bank reconciliation processing >3 000 transactions and >4 000 documents with multi-model OCR, LLM-based document separation, web UI and loop-based prompt versioning; infrastructure: Docker Compose, PostgreSQL, Ollama LLM server (Aug 2025 — present).
Supplier development for suspension parts including changes, new launches, relocations and bottleneck management for KTM (May 2023 — Nov 2024).
Leading transfer of 150 serial production items after supplier plant closure for KTM (Jul 2022 — Jan 2024).
Lean and process consultation for Swiss manufacturer of electro components at Von Roll Schweiz AG (Jun 2018 — Nov 2018).
Ongoing back office support and process improvement for a private pain therapy & TCM practice including anesthesia protocols, website setup, digitalization and process optimization (May 2018 — May 2025).
Acting plant manager for interior trim components in Hungary focusing on stabilization, tooling optimization, headcount reduction and backlog reduction at MAO Automotive GmbH & Co (Feb 2018 — Jun 2018).
Start-up consulting and coaching for private pain therapy & TCM practice covering concept, business plan, financing, construction, setup and SOP implementation (May 2017 — Apr 2018).
Program manager for critical suppliers DAG BR238 door trim leading supplier qualification, sampling, PPAP/EMPB tracking and process approvals at Megatech Industries Deutschland GmbH (Jul 2016 — Oct 2017).
Interim COO and acting plant manager in aluminum profile processing automotive supplier with 350 employees and €80 M revenue, leading expansion in Slovakia at PWG Profilrollen-Werkzeugbau GmbH (Sep 2015 — May 2016).
Leading labor and material efficiency program for headliner production focusing on process, material, labor and supplier cost reduction at Motus Headliner GmbH (Jan 2015 — Aug 2015).
Leading development program for interior door trim DAG C292 including launch phase onsite in MS & AL at Toyota Boshoku America (Sep 2012 — Jan 2015).
Leading cost down team for DAG C218 door panels at Toyota Boshoku Europe NV (Mar 2012 — Aug 2012).
Sun visor development consulting for project team in Turkey including optimization, market analysis and design reviews at MARTUR FOMPAK (Jan 2012 — Dec 2014).
Leading transfer of injection molding tools to new suppliers including sampling, assembly trials and PPAP/EMPB coordination at Toyota Boshoku Europe NV (Jan 2012 — Aug 2012).
Program manager for BMW sun visors L7 Platform with production transfer and new development of F30 sun visor at Magna (Jul 2010 — Dec 2011).
Noushiq Mohammed K A N
Last position:
Projects at Institute for Intelligent Systems
- Evaluation and analysis of camera-based traffic light and sign recognition system on various LLM-based autonomous driving systems (LMDrive, BEVDriver)
- Implemented VLM based traffic notice instruction generation unit for closed-loop autonomous driving system which alerts driver in unforeseen driving incidents
- Developed independent LLM-based local chatbot with Llama, DeepSeek and Qwen including MLflow evaluation framework
Martin Ratajczak
Last position:
Senior LLM Research Scientist at BYO Inc.
- Research and develop models for chatbots, NLP and LLMs (e.g. Llama, Qwen, OpenAI)
- Enhance chatbots with RAG, in-context learning
- Supervised fine-tuning (PEFT, LoRA), Huggingface or Unsloth
- Advanced training methods: Test-time training, (transductive) active learning, reinforcement learning
- High-throughput serving with vLLM
- Apply embedding models (e.g. SentenceTransformers), similarity/vector search or vector DB or ranking (e.g. LlamaIndex, Faiss, LangChain)
- Generate and filter synthetic data, clustering
- Detect hallucinations
- Evaluate chatbot models (Rouge, BLEU, F1-Score, Recall, Precision)
- Visualization of experiments (matplotlib)
Stephan Martin
Last position:
Sabbatical, professional development at Self-employed
- Further training in Snowflake and Google Looker
- Working with LLMs: local models (Llama, Mistral, Gemma, Phi, Qwen, DeepSeek, Bitnet, Flux, Whisper), OpenAI API, frontends (ollama, openwebui, loacalai)
- Inference methods: llama.cpp, vLLM, transformer
- Quantization, benchmarking, prompting
- LLM Agents (Tool/Function Calling, LangChain, LangGraph, MCP)
- Topics: attention, reasoning, chain of thoughts, RAG, GraphRAG, mlflow
- Cloud hosted: ChatGPT, Claude, Gemini
Maciej Modrzejewski
Last position:
AI & Machine Learning Consultant at Self-employed
- Led the technical implementation of several AI products for companies, including defining the software architecture, leading distributed development teams of ML and software engineers, and coordinating delivery with executives.
- Developed and delivered 5+ production-ready AI products in the areas of machine translation, speech AI, document AI, conversational AI, and AI quality evaluation.
- Built a multilingual machine translation platform with over 550 production-ready models for automated translation of documents and business content in more than 40 languages.
- Built production-ready Conversational AI platforms using self-hosted Large Language Models (Qwen) with RAG pipelines, prompt engineering, tool calling, and secure enterprise deployments for internal knowledge assistants and customer-facing chatbots.
- Developed AI orchestration frameworks for dynamic selection of foundation models and for optimizing the quality, latency, robustness, and cost of production AI systems.
- Developed automated evaluation and monitoring pipelines for continuous quality assessment of Conversational AI systems, speech AI, and Large Language Models.
Discover over 15,000 top freelancers
Statistics of experts using Qwen
Aggregated from the professional profiles of matched freelancers.
Experience
19 years
Position duration
2.5 years
Positions per freelancer
8
Top business areas
Product Development, Information Technology, Project Management
Top industries
Information Technology, Manufacturing, Automotive
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
100%
Master's degree or higher
83%
Doctorate
17%
Certifications per freelancer
8
Most common languages
German, English, Spanish
Speak two or more languages
88%
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 Qwen
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
Qwen use cases
Qwen is a family of large language models used for chat, search, extraction and content generation. Companies bring in specialists to build assistants, automate document work and connect the model to internal knowledge. It is also used for multilingual products and domain-specific workflows.
Ecosystem
Qwen projects often sit on top of Python, APIs, vector databases and orchestration tools. Strong specialists know how to work with prompt templates, function calling, retrieval pipelines and model routing.
- Build chat and support assistants
- Add retrieval over company data
- Automate text extraction and summarization
- Tune prompts and evaluation sets
When to hire
Bring in freelance expertise when a team needs help moving from demo to production. Common signs are unstable answers, poor grounding, weak prompt design or unclear model selection between Qwen, open-weight alternatives and hosted services. In Germany, this is often useful for teams that need English and German output in the same workflow.
What strong specialists do
Good Qwen professionals test outputs, reduce hallucinations and shape the system around real business tasks. They think about context limits, safety filters, latency and cost, not just prompts. They also document how to maintain the solution after launch.
Delivery focus
Most engagements center on a clear deliverable: a working prototype, a knowledge assistant, a text pipeline or an evaluation harness. Some projects also include fine-tuning, tool use, or migration from older Tongyi Qianwen implementations to the current Qwen lineup.
- Define prompt and tool behavior
- Connect data sources and retrieval
- Measure answer quality
- Prepare handover for internal teams
Collaboration in Germany
Qwen work can be done fully remote, but on-site sessions help when the model must align with local processes, sensitive data or German-language review cycles. Freelance specialists who communicate clearly and work with product, data and security teams usually fit best. They should explain trade-offs in plain language and keep the scope tight.
Frequently asked questions
Questions about Qwen? Start with the answers below.
Qwen is used for assistants, search over internal documents, content drafting and structured text extraction. Teams also use it for multilingual workflows, tool calling and retrieval-augmented generation when the model needs current company knowledge. It fits best when the output must be useful inside a product or business process, not just a chat demo.
Qwen is often chosen when a team wants strong open-weight options and more control over deployment. Compared with ChatGPT, it gives more flexibility for custom hosting and integration. Compared with Llama, the decision often comes down to model quality on the target language, tool use and the surrounding ecosystem.
A strong Qwen specialist usually knows prompting, Python, API integration and retrieval design. Useful adjacent skills include vector search, evaluation, guardrails and basic MLOps. If the work touches product data, experience with privacy review and logging is a plus.
A small proof of concept can start with a Qwen specialist who has shipped similar language-model work before. Production systems need someone who can handle evaluation, failure modes and rollout planning. If the project depends on sensitive data or multilingual behavior, avoid treating it as a simple prompt task.
Yes, Qwen work is often done remotely, especially for prompt design, retrieval setup and evaluation. For teams in Germany, remote collaboration works well when communication is structured and requirements are clear. On-site workshops can still help at the start of a project or during stakeholder reviews.
Qwen is the name most teams now use for the model family, while Tongyi Qianwen is the earlier name many people still search for. In hiring, both terms matter because specialists may list either one on their profiles. A good candidate should understand the same underlying model line and how it evolved.
Ask for examples of production work, not just prompts. A good Qwen freelancer can explain evaluation methods, show how they reduced bad answers and describe the retrieval or tool setup in detail. Clear reasoning, clean handover notes and realistic trade-offs are better signs than polished demos.
Typical Qwen deliverables include a working assistant, a retrieval pipeline, prompt templates, test cases and evaluation results. Some projects also include fine-tuning or migration from a previous Qwen setup. The best freelancers define success in terms of business tasks, not model features alone.
The average hourly rate of freelancers in Germany who have used Qwen in their recent projects is 128 €, which corresponds to a daily rate of about 1,023 € based on an 8-hour working day.
Of the freelancers in Germany who have used Qwen in their recent projects, 100% hold at least a Bachelor's degree, 83% hold at least a Master's degree, and 17% hold a doctorate.
On average, freelancers in Germany who have used Qwen in their recent projects have 19 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 Qwen in their recent projects are German (100%), English (88%), and Spanish (38%).
The most common industries among freelancers in Germany who have used Qwen in their recent projects are Information Technology (88%), Manufacturing (63%), and Automotive (38%).
The most common business areas among freelancers in Germany who have used Qwen in their recent projects are Product Development (100%), Information Technology (75%), and Project Management (75%).
Main locations of FRATCH Experts, who have recently used Qwen
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
