Large Language Model Experts in Nuremberg
matched in minutes by AIHire experts who design prompt systems, integrate GPT and other LLM APIs, and deliver reliable retrieval-augmented applications. FRATCH matches you quickly with vetted, available freelance professionals whose skills fit your project.
Meet FRATCH Experts in Nuremberg, who have recently used Large Language Model
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.
Arun Sai T.
Last position:
AI-Backend Developer Intern at Calvergy UA
- Integrated complex AI-based energy system models into the frontend framework, enabling the visualization of insights for 6+ key clients and maximizing energy utilization.
- Maximized energy efficiency and utilization by architecting the seamless data flow between AI models and the user interface for rapid, actionable reporting.
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.
Elnazossadat H.
Last position:
Data Analyst at Siemens Healthineers
- Developed KPI dashboards using Power BI and DAX for 4+ business units, improving reporting transparency and strategic decision support.
- Migrated enterprise finance data views into dbt models, implementing modular SQL transformations, version-controlled data pipelines, and automated documentation to create a scalable analytics layer.
- Built dimensional data models in Snowflake for enterprise finance data, enabling scalable forecasting and supporting executive decision-making.
- Designed end-to-end ETL/ELT pipelines using Snowflake and SAP HANA, integrating data from 3+ enterprise systems.
- Automated monthly reporting workflows using SQL and Power BI, delivering strong business impact by reducing manual effort by 80%.
- Collaborated with finance stakeholders to translate business requirements into analytical data models, supporting strategic decision-making cycles.
- Delivered ad-hoc financial reports using Power BI, reducing turnaround time by 60%.
- Implemented data validation logic in SQL, resolving 95% of recurring data quality issues.
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.
Pawan S.
Last position:
CAPTCHA Recognition using CRNN
- Built a CRNN model with VGG16 and BiLSTM backbone for text-based CAPTCHA recognition
- Achieved 9.37% character error rate and 68.36% sequence accuracy on validation data
- Expanded data augmentation pipeline with distortions, noise injection, and clutter to improve robustness
- Conducted detailed error analysis on confusable characters (O, Q, D) and proposed error-specific augmentation
- Tech Stack: Python, TensorFlow/Keras, OpenCV, NumPy, Matplotlib
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
Uddipan B.
Last position:
Research Team Member at Munich Music Labs, TUM
- Focused on exploring the intersection of Music and AI.
Ashmi J.
Last position:
Software Developer at Myrix Labs
- Engineered high-performance APIs with FastAPI + MongoDB, integrating live weather data (NOAA, NWS).
- Developed an AI chatbot with OpenAI APIs — context-aware by location, profession & interests.
- Created admin dashboard APIs for real-time monitoring and zero-downtime configuration.
- Integrated Stripe Embedded Payments with secure transactions & subscription management via webhooks.
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
Samuel A.
Last position:
Bachelor thesis 'Development of an AI-based assistant system for personalized competence development for IT professionals' at N-ERGIE
- Design and development of an AI-based assistant system for targeted identification and closing of knowledge gaps in software development at an energy provider
- Technology stack:
- Vector database (Qdrant Cloud)
- RAG for semantic document analysis
- FastAPI backend to process user requests
- Integration with an LLM (e.g., OpenAI)
Discover over 15,000 top freelancers
Statistics of experts using Large Language Model
Aggregated from the professional profiles of matched freelancers.
Experience
10 years (Germany: 15 years)
Position duration
1.9 years (Germany: 2.9 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: 96%)
Master's degree or higher
88% (Germany: 70%)
Certifications per freelancer
2 (Germany: 3)
Most common languages
English, German, Hindi
Speak two or more languages
100% (Germany: 97%)
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 Large Language Model
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.
Large Language Model 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 (94%)
- Manufacturing (50%)
- Automotive (38%)
- Education (38%)
- Banking and Finance (25%)
- Professional Services (25%)
- Retail (25%)
- Energy (19%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What LLMs do
Large Language Models process and generate human language. Companies use LLMs to build conversational assistants, document search, summarisation tools, content workflows and software features that interpret text. They can work with structured data, images or audio when combined with suitable models and services.
Core capabilities
Strong specialists connect model behaviour to a clear business task. Their work may include:
- Designing prompts, system instructions and evaluation criteria
- Integrating GPT, Claude, Gemini or open-weight models through APIs
- Building retrieval-augmented generation with company knowledge
- Creating tool use, function calling and structured outputs
- Adding guardrails, moderation and human review
Ecosystem and tooling
LLM projects span model providers, orchestration libraries and application infrastructure. Common components include vector databases, embeddings, LangChain, LlamaIndex, Python or TypeScript services, REST APIs and cloud runtimes. Specialists also work with data pipelines, observability tools, model gateways and access controls.
When to bring in experts
Freelance expertise helps when a prototype must become a dependable product, or when an existing chatbot gives inconsistent answers. Companies also seek specialists for private knowledge bases, multilingual support, prompt migration, model selection and production monitoring. In Nuremberg, remote collaboration can extend local product, manufacturing and service teams without limiting the search to one office.
Reliable production work
A strong professional treats an LLM as one part of a larger system. They define the source data, failure states and user permissions before choosing a model. They test factuality, latency, privacy and cost, then document prompts, datasets, fallback paths and release decisions so the application remains maintainable.
Choosing the right specialist
Look for evidence of shipped LLM features rather than demonstrations alone. Ask how the specialist evaluates answers, handles sensitive information and responds when the model is uncertain. The right fit combines language-model knowledge with backend integration, data quality, security and product judgement, and communicates clearly with stakeholders in German or English when needed.
Frequently asked questions
Key details about Large Language Model, drawn from the questions we get asked most.
A Large Language Model can power support assistants, internal search, document extraction, summarisation, drafting and natural-language interfaces. The strongest applications connect the model to trusted business data and define clear review or escalation paths.
An LLM handles varied language and ambiguous requests more flexibly than fixed rules. Traditional software remains better for deterministic calculations and strict workflows, so many useful products combine both approaches.
A strong Large Language Model specialist usually understands API integration, Python or TypeScript, data preparation, vector search and cloud deployment. Experience with security, observability, evaluation design and user experience is also valuable for production systems.
A simple proof of concept may need focused model and API knowledge, while a production system requires broader expertise. A Large Language Model professional should be able to assess data quality, privacy, retrieval, testing and fallback behaviour before estimating the work.
Yes, most LLM work can be handled remotely through shared repositories, secure environments and regular product sessions. On-site workshops in Nuremberg can still help with process discovery, stakeholder alignment and access to operational knowledge.
An LLM specialist should compare quality, data handling, latency, infrastructure effort and operational control. Open-weight models can suit private or highly customised workloads, while hosted APIs often reduce deployment and maintenance work.
Ask for a clear evaluation method, representative test cases and an explanation of known failure modes. A capable Large Language Model professional discusses grounding, hallucinations, prompt changes, monitoring and user feedback instead of presenting a demo as proof.
A Large Language Model delivery may include prompt and model selection, data or document pipelines, retrieval, API services, guardrails and an evaluation set. It should also include deployment guidance, monitoring and documentation for the team that will maintain it.
The average hourly rate of freelancers in Nuremberg, Germany who have used Large Language Model in their recent projects is 54 €, which corresponds to a daily rate of about 434 € based on an 8-hour working day.
Of the freelancers in Nuremberg, Germany who have used Large Language Model in their recent projects, 100% hold at least a Bachelor's degree and 88% hold at least a Master's degree.
On average, freelancers in Nuremberg, Germany who have used Large Language Model in their recent projects have 10 years of professional experience, with a single engagement typically lasting around 1.9 years.
The most common languages among freelancers in Nuremberg, Germany who have used Large Language Model in their recent projects are English (100%), German (94%), and Hindi (25%).
The most common industries among freelancers in Nuremberg, Germany who have used Large Language Model in their recent projects are Information Technology (94%), Manufacturing (50%), and Automotive (38%).
The most common business areas among freelancers in Nuremberg, Germany who have used Large Language Model in their recent projects are Information Technology (94%), Product Development (88%), and Research and Development (88%).
Main locations of FRATCH Experts, who have recently used Large Language Model
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 
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Frankfurt 
Stuttgart 
Dusseldorf 
Dortmund 
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FRATCH CEO
