Generative AI Experts in Nuremberg
matched in minutes by AIHire experts who design retrieval-augmented generation systems, integrate foundation models and build reliable AI assistants for your products and operations. FRATCH connects you with vetted, available freelancers through fast, precise AI matching.
Meet FRATCH Experts in Nuremberg, who have recently used Generative AI
Oleg O.
Last position:
Senior Software Developer / BI Integration Developer Power BI, C# at Telecommunications
Embedded Analytics & AI-assisted BI
Design and development of an integrated analytics solution based on ASP.NET Core, Power BI Embedded, and LLM services to provide context-based business information.
Development of an AI agent with Function/Tool Calling for the secure orchestration of REST APIs, SQL data sources, and technical services within defined business processes.
Building automated BI workflows including workspace management, deployment processes, and scheduled refresh via the Power BI REST API.
Implementation of secure service-to-service communication with Microsoft Entra ID and Service Principal, and integration into existing enterprise system landscapes.
Technologies: ASP.NET Core, C#/.NET, Power BI Embedded, Power BI REST API, LLM API, AI Agents, Function/Tool Calling, Entra ID
David O.
Last position:
Research Intern at Pattern Recognition Lab
- Spearheaded the integration of a custom Transformer-based encoder into the AFFGANwriting pipeline, replacing the legacy VGG19 architecture to capture richer, high-fidelity writer-style representations.
- 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.
- Enhanced OCR performance by 20% by implementing a teacher-student framework that leveraged a TrOCR benchmark model for auxiliary training alignment
Partha N.
Last position:
AI Software Developer at Fraunhofer IIS
- Built a custom AI chatbot for an e-commerce client using GPT-4 and LangChain with RAG, reducing customer support ticket volume by 45% and improving response accuracy to 92%.
- Designed and deployed an intelligent document processing system using LlamaIndex, Pinecone, and FastAPI for a FinTech startup, enabling semantic search across 100K+ financial documents.
- Developed multi-agent AI workflows using CrewAI and LangGraph for a marketing agency, automating lead research, content generation, and outreach — saving 20+ hours/week of manual work.
- Created AI-powered automation pipelines using n8n, Make, and Zapier integrated with CRMs (GoHighLevel, HubSpot), reducing manual data entry by 80% for a real estate firm.
- Delivered prompt engineering and LLM fine-tuning consulting for multiple clients, optimizing AI model outputs for customer support, content creation, and data extraction use cases.
- Built production-ready REST APIs with Python and FastAPI to serve AI models on AWS and GCP, handling 10K+ daily requests with 99.9% uptime.
Muntaha S.
Last position:
AI Engineer (Freelance) at Upwork
- Delivered 40+ AI projects and 23 strategic consultations for international clients (US, Europe, Middle East), achieving a 98% job success rate and building long-term partnerships.
- Developed and deployed production-grade AI solutions in computer vision, NLP, deep learning, and generative AI (LLMs, RAG pipelines, Stable Diffusion, OCR, chatbots), enabling automation and improving client efficiency by up to 70%.
- Designed and fine-tuned large language models (LLMs), including prompt engineering and integration with enterprise knowledge bases, leading to smarter decision-making and reduced manual effort.
- Built real-time computer vision applications (detection, segmentation, OCR) and integrated them into business systems, significantly enhancing accuracy and scalability.
- Consulted startups and enterprises on AI strategy, architecture, and deployment (cloud & on-premise), accelerating product development and reducing time-to-market.
- Managed complete AI project lifecycles (requirements gathering, solution design, deployment, support) in agile, international, and cross-functional environments, ensuring high-quality delivery.
Kashyap K.
Last position:
Master’s Thesis - Synthetic Data Generation for Quality Inspection at Schaeffler Technologies AG
- Developed a synthetic data generation framework using 3D simulation (NVIDIA Omniverse) and Generative AI (Stable Diffusion) to model and augment industrial surface defects.
- Trained and evaluated Computer Vision models (YOLO, DETR), achieving 94% detection accuracy on real-world samples and demonstrating successful simulation-to-reality transfer.
- Applied domain adaptation to improve simulation-to-reality transfer, enabling scalable Industrial AI for automated quality inspection and reducing manufacturing downtime.
Tobias V.
Last position:
Managing Partner at Unwritten GmbH
- Pioneer work in personalized AI: development of a framework for “Interactive Content” (RAG) for novels, lectures, expert debriefing
- Successful launch of Einbug, the Pantopia chatbot, with media resonance (SZ interview)
- Creation of compelling AI personalities: AI blog ([link]), 100% personalized learning environments, Perry Rhodan, and others.
Puranjan B.
Last position:
Internship - Generative AI at Continental
- Gathered tire images and their feature descriptions.
- Cleaned dataset of image metadata using pandas.
- Stored image feature embeddings in Chroma vector db.
- Used image augmentations to increase dataset size.
- Used sklearn to create shuffled datasets and imbalanced-learn to balance class sizes in dataset.
- Used PyTorch to train and test different neural networks.
- Validated model using custom accuracy metric based on similarity search in ChromaDB.
- Visualized accuracy predictions using matplotlib.
- Plugged trained model into DreamBooth to train stable diffusion model and generate new images of tires.
- Created custom Docker image in Amazon Elastic Container Registry for machine learning script.
Ralph N.
Last position:
AI Lead Engineer Car Configurator for leading German premium manufacturer at e-ntegration GmbH
- Intent-driven approach to configure all models across all series automotive in all distribution markets of this car manufacturer
- Developed a customer-facing, conversation-driven integration layer to achieve 100% hallucination-free technical configurations
- Utilized Microsoft Azure AI Services: AI Foundry, Agent Service, AI Search; Prompt Shield Services; Content Security; Terraform; API Gateway; AI Gateway; Container Services; Azure Agent SDK; Agent Skills; RAG; MCP Servers and tools
Ekaansh K.
Last position:
Master thesis - LLM powered RAG System at Friedrich-Alexander-Universität Erlangen-Nürnberg
- Developed a RAG system to automate student queries with 96% accuracy, built using FastAPI and LangChain and deployed on the university server with Docker.
- Evaluated performance using RAGAS, comparing LLMs (Llama3.3, Llama3.1, GPT-4o-mini), vector embeddings, and various retrieval techniques within the RAG pipeline.
- Technical Skills: Python, FastAPI, Docker, AWS, LangChain, LangSmith, NLP, HTML, CSS
Ralf T.
Last position:
Director Global Cloud - Quality & Compliancy at Eviden
- Leading a global team for quality assurance and compliance in cloud services
Discover over 15,000 top freelancers
Statistics of experts using Generative AI
Aggregated from the professional profiles of matched freelancers.
Experience
15 years (Germany: 16 years)
Position duration
2.4 years (Germany: 2.3 years)
Positions per freelancer
8 (Germany: 10)
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Manufacturing, Automotive
Certification focus areas
Information Technology, Research and Development, Business Intelligence
Bachelor's degree or higher
100% (Germany: 97%)
Master's degree or higher
90% (Germany: 75%)
Certifications per freelancer
2 (Germany: 3)
Most common languages
English, German, Bangla
Speak two or more languages
100% (Germany: 98%)
Based on our profile pool as of 19 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Nuremberg 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 Nuremberg using Generative AI
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 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Generative AI 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 (90%)
- Manufacturing (60%)
- Automotive (40%)
- Education (30%)
- Professional Services (30%)
- Banking and Finance (20%)
- Retail (20%)
- Advertising (10%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Generative AI does
Generative AI creates new content from patterns learned in large datasets. It can produce text, code, images, audio and structured responses from natural-language prompts. Companies use it to power assistants, automate document work, support research and add intelligent features to existing software.
Products and use cases
Common delivery areas include:
- Customer and employee assistants grounded in company knowledge
- Document extraction, summarisation and classification
- Content, code and product-description generation
- Semantic search and recommendation experiences
- Synthetic data and creative image workflows
The right design depends on data sensitivity, response quality, latency and how much control the business needs.
Models and tooling
The ecosystem includes foundation models from OpenAI, Anthropic, Google and open-source communities, accessed through APIs or hosted in controlled environments. Specialists work with embeddings, vector databases, prompt templates, tool calling, fine-tuning and retrieval-augmented generation. They also connect model workflows to Python or TypeScript services, cloud infrastructure and existing business systems.
When to bring in expertise
Companies often need freelance support when a prototype must become a dependable product, internal data needs safe grounding or model costs and output quality are difficult to control. A specialist can evaluate models, define an architecture, create evaluation sets and establish monitoring before a solution reaches users. This is useful for teams in Nuremberg that need remote delivery or occasional on-site collaboration.
Delivery and governance
Strong implementation covers more than a convincing demo. It includes access control, privacy by design, prompt-injection defence, provenance, human review and clear fallback behaviour. Professionals should document data flows, test responses against realistic cases and measure quality with repeatable evaluations. They also account for regional language needs when assistants serve German-speaking users.
What distinguishes strong specialists
Look for professionals who can connect model capabilities to a clear business process. They explain trade-offs between hosted APIs, open models, retrieval and fine-tuning without treating any option as universal. Good specialists leave behind maintainable prompts, test suites, deployment documentation and a practical plan for improving the system as data, models and user expectations change.
Frequently asked questions
The facts hiring teams ask for most often when it comes to Generative AI.
Generative AI is used for assistants, document processing, semantic search, content creation, code support and workflow automation. A freelancer can connect these capabilities to internal data and business rules rather than delivering an isolated chat interface.
Generative AI creates flexible responses and new content, while traditional automation follows predefined rules and search primarily retrieves existing information. Retrieval-augmented generation combines search with model output, helping a system answer questions using approved company sources.
A strong Generative AI specialist often works with APIs, Python or TypeScript, cloud services, vector databases, data pipelines and user-facing application design. Security, evaluation methods and prompt-injection defence are also important for production systems.
The required background depends on the scope. A proof of concept may need strong model and application knowledge, while a customer-facing system also requires experience with monitoring, access control, evaluation, data governance and reliable deployment.
Yes. Generative AI work is often suitable for remote collaboration through shared repositories, cloud environments and structured review sessions. On-site workshops in Nuremberg can still help when teams must map sensitive processes, agree on data access or train users.
The choice depends on privacy requirements, response quality, operating effort, integration needs and cost control. Generative AI specialists should test relevant models on representative company tasks instead of choosing only by public benchmarks or brand familiarity.
Ask for task-specific evaluation cases, failure analysis and evidence that outputs remain grounded in approved sources. A reliable Generative AI implementation also has clear escalation paths, access controls, monitoring and documented behaviour when the model is uncertain.
Generative AI systems serving German-speaking users should be tested with local terminology, formal and informal language, compound nouns and domain-specific documents. The specialist should also check whether prompts, retrieval content and safety rules behave consistently across German and English workflows.
The average hourly rate of freelancers in Nuremberg, Germany who have used Generative AI in their recent projects is 66 €, which corresponds to a daily rate of about 529 € based on an 8-hour working day.
Of the freelancers in Nuremberg, Germany who have used Generative AI in their recent projects, 100% hold at least a Bachelor's degree and 90% hold at least a Master's degree.
On average, freelancers in Nuremberg, Germany who have used Generative AI in their recent projects have 15 years of professional experience, with a single engagement typically lasting around 2.4 years.
The most common languages among freelancers in Nuremberg, Germany who have used Generative AI in their recent projects are English (100%), German (90%), and Bangla (10%).
The most common industries among freelancers in Nuremberg, Germany who have used Generative AI in their recent projects are Information Technology (90%), Manufacturing (60%), and Automotive (40%).
The most common business areas among freelancers in Nuremberg, Germany who have used Generative AI in their recent projects are Information Technology (90%), Product Development (80%), and Research and Development (80%).
Main locations of FRATCH Experts, who have recently used Generative AI
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.
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Hamburg 
Munich 
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Frankfurt 
Stuttgart 
Dusseldorf 
Dortmund Request a free demo
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FRATCH CEO
