Large Language Model Experts in Nuremberg
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Meet FRATCH Experts in Nuremberg, who have recently used Large Language Model
Oleg Orlov
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 contextual business information.
Development of an AI agent with Function/Tool Calling for secure orchestration of REST APIs, SQL data sources, and technical services within defined business processes.
Build-up of 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, as well as 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 Onaiyekan
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 Nandi
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 Thunga
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 Shams
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.
Tobias Von Dewitz
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 Hosseininia
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 Bandyopadhyaya
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 Saxena
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
Simone Pfliegel
Last position:
Independent Expert and Assessor at EIT Culture & Creativity
- Independent expert and assessor for the European Institute of Innovation and Technology (EIT), Culture & Creativity
- Review and assessment of European innovation and funding applications based on defined quality and selection criteria
- Evaluation of relevance, level of innovation, feasibility, impact, scalability, and sustainability
- Professional focus areas: artificial intelligence, EdTech, digital transformation, education, innovation, and creative industries
- Analysis of complex project concepts, consortia, impact strategies, and European cooperation projects
- Preparation of well-founded, clear evaluation and selection assessments Experience at the intersection of education, technology, innovation, and European funding programs
Ralph Navasardyan
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 Basu Bir
Last position:
Research Team Member at Munich Music Labs, TUM
- Focused on exploring the intersection of Music and AI.
Ashmi Jha
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 Khosla
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 Arapoglu
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
11 years (Germany: 15 years)
Position duration
1.9 years (Germany: 2.9 years)
Positions per freelancer
8 (Germany: 9)
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Manufacturing, Education
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: 72%)
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 30 Aug 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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
What it is
Large Language Models, or LLMs, are systems that understand and generate text. Companies use them for assistants, search over documents, drafting, classification, and workflow automation. Strong professionals know where the model helps and where guardrails are needed.
Typical work
- Chat and knowledge assistants
- Retrieval-augmented generation over company content
- Prompt design and tool use
- Output checks, logging, and feedback loops
These experts turn a model into a usable product, not just a demo. They shape inputs, outputs, and failure handling so the system fits real business work.
Ecosystem
LLM work often sits next to Python, APIs, vector databases, and orchestration tools. Common choices include OpenAI, Anthropic, Azure OpenAI, LangChain, and LlamaIndex, plus open-source models such as Llama and Mistral. Good specialists know how these pieces connect and when simpler patterns are better.
When to bring help
Teams usually need freelance expertise when they want a fast pilot, need to connect an LLM to private data, or must improve unreliable answers. In Nuremberg, this often matters for industrial, logistics, and software teams that want German and English support in the same workflow. It also helps when internal teams need review, testing, or a clean handover.
What strong specialists do
Strong professionals write clear prompts, design retrieval flows, test quality with real examples, and reduce hallucinations. They think about latency, data privacy, and cost from the start. They also document decisions so your team can maintain the system after launch.
How to choose
Look for work on real products, not only experiments. Ask how the specialist measures answer quality, handles source citations, and protects sensitive data. For larger rollouts, a good LLM expert can shape architecture, collaboration, and review steps without overcomplicating the system.
Frequently asked questions
Key details about Large Language Model, drawn from the questions we get asked most.
A Large Language Model is used to generate and analyze text, answer questions, summarize documents, and support workflow automation. In practice, companies use it for support assistants, internal search, draft creation, and triage of incoming requests. The best use cases are the ones where language work is repetitive and rules are clear.
A Large Language Model is more flexible than rule-based automation because it can handle varied wording and messy input. Rule-based systems are still better when the output must be exact and the logic is narrow. Many projects use both: rules for hard constraints and the model for language understanding.
GPT is one well-known family of Large Language Model systems, but it is not the only one. People also work with models from OpenAI, Anthropic, Google, Meta, and other providers, plus open-source options like Llama and Mistral. When you hire help, ask which model fits your data, latency needs, and privacy rules.
A strong Large Language Model specialist usually also knows Python, API design, retrieval systems, and testing. Many projects need vector search, document parsing, and basic cloud setup as well. If the work touches customer data, privacy and access control matter too.
A Large Language Model project can start small, but it still needs someone who has shipped beyond a prompt demo. For a pilot, one focused specialist can often cover prompt design, retrieval, and evaluation. For a production rollout, you want experience with quality checks, monitoring, and failure handling.
Yes. Most Large Language Model work can be done remotely because the core tasks are design, integration, testing, and review. If your project involves sensitive documents or local stakeholders in Nuremberg, hybrid collaboration can help with access, alignment, and handover.
Look for clear examples of shipped systems, not just prompt experiments. A strong Large Language Model freelancer can explain how they measure answer quality, reduce hallucinations, and keep the system useful when inputs change. Good documentation and clean handover are also strong signs.
Ask what problem the Large Language Model must solve, what data it may use, and how success will be checked. Also ask how the freelancer handles source grounding, edge cases, and user feedback. If the answer is vague, the project is not ready yet.
The average hourly rate of freelancers in Nuremberg, Germany who have used Large Language Model in their recent projects is 62 €, which corresponds to a daily rate of about 496 € 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 11 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 (24%).
The most common industries among freelancers in Nuremberg, Germany who have used Large Language Model in their recent projects are Information Technology (94%), Manufacturing (47%), and Education (41%).
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 
Cologne 
Frankfurt 
Stuttgart 
Dusseldorf 
Dortmund 
Essen Request a free demo
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Philipp Thomaschewski
FRATCH CEO
