Fine-Tuning Experts in Nuremberg
in minutes from over 15,000 CVs with the power of AI.Hire experts who adapt foundation models, prepare training data, and run evaluation loops for fine-tuning work. From instruction tuning to domain-specific prompt behavior and model quality checks, FRATCH matches you fast with vetted, available freelancers.
Meet FRATCH Experts in Nuremberg, who have recently used Fine-Tuning
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.
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
Uddipan B.
Last position:
Research Team Member at Munich Music Labs, TUM
- Focused on exploring the intersection of Music and AI.
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
Discover over 15,000 top freelancers
Statistics of experts using Fine-Tuning
Aggregated from the professional profiles of matched freelancers.
Experience
10 years (Germany: 11 years)
Position duration
2.1 years (Germany: 1.9 years)
Positions per freelancer
5 (Germany: 8)
Top business areas
Product Development, Research and Development, Information Technology
Top industries
Information Technology, Education, Manufacturing
Certification focus areas
Information Technology, Research and Development, Business Intelligence
Bachelor's degree or higher
100% (Germany: 98%)
Master's degree or higher
100% (Germany: 78%)
Certifications per freelancer
2 (Germany: 3)
Most common languages
German, English, 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 Fine-Tuning
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.
Fine-Tuning 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 (86%)
- Education (43%)
- Manufacturing (43%)
- Automotive (29%)
- Banking and Finance (29%)
- Healthcare (29%)
- Professional Services (29%)
- Retail (29%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What fine-tuning does
Fine-tuning adapts a pre-trained model to a specific task, domain, or style. Companies use it when a base model is close, but not precise enough for their data, tone, or output format. It is common in chat assistants, classification, extraction, and domain-specific generation.
Common project work
- Prepare and clean task-specific training data
- Run supervised fine-tuning and evaluate outputs
- Adjust prompts, labels, and output schemas
- Compare fine-tuned models with prompt-only baselines
- Document rollback and release steps for production use
Tools and methods
Strong specialists know the model family, the training stack, and the evaluation process. They work with Hugging Face, PyTorch, LoRA, PEFT, and common experiment tracking tools. For some projects, they also handle data versioning, safety filters, and deployment checks.
When companies bring in experts
Teams usually need outside help when a base model gives unstable results, domain language is too specific, or internal staff do not have time to tune and test properly. In Nuremberg, this often fits work with industrial data, enterprise support workflows, and multilingual content. Remote collaboration works well when data access and review steps are clear.
What strong specialists deliver
Good fine-tuning work is measured by stable outputs, clear evaluation, and clean documentation. Strong professionals explain trade-offs between prompt engineering, retrieval, and tuning, then recommend the simplest option that meets the goal. They also leave behind repeatable training and review steps.
How to scope the work
Start with the target behavior, the available data, and the model you want to improve. Define success cases, failure cases, and the checks that will block a bad release. If the task depends on sensitive data or German-language content, make that clear early so the specialist can shape the approach.
Frequently asked questions
Quick answers to the questions that come up most around Fine-Tuning.
Fine-Tuning is used to adapt a pre-trained model to a specific domain, tone, or output format. Companies use it for support replies, document extraction, classification, and specialized generation where prompt changes alone are not enough. It is also useful when the same behavior must be repeated across many requests.
Fine-Tuning changes the model itself, while prompt engineering changes the instructions and RAG adds external context at runtime. If the task depends on style, structure, or repeated behavior, tuning can be the better fit. If the issue is missing facts, retrieval is often the first thing to try.
A Fine-Tuning specialist is useful when a base model is close but inconsistent, or when the task needs domain language, strict formatting, or better classification quality. They are also valuable when teams need to compare tuning with prompt-only and retrieval-based options. Early expert input can save time by avoiding unnecessary training work.
A strong Fine-Tuning freelancer usually understands data preparation, evaluation design, and model behavior. Helpful adjacent skills include Python, PyTorch, Hugging Face, experiment tracking, and working with structured training data. For production work, deployment awareness and safety review also matter.
Yes, many Fine-Tuning projects work well remotely if the data access, review process, and security rules are clear. In Nuremberg, some companies prefer mixed collaboration when subject matter experts need to review outputs in person. The key is a clean workflow for feedback and approval.
Look for someone who can explain data choice, evaluation, and failure cases clearly. A strong Fine-Tuning expert shows how they compare results against a baseline and how they decide whether tuning is worth it. Good documentation and repeatable steps are a strong sign of quality.
Not always. A Fine-Tuning project can start with a focused, well-labeled set if the task is narrow and the output is easy to judge. The important part is data quality, consistent labels, and a clear definition of success.
A Fine-Tuning freelancer should ask what the model must do, what data is available, and how success will be reviewed. They should also clarify privacy rules, target formats, and whether the team expects a tuned model, a prompt design, or a comparison of both. Clear scope at the start avoids rework later.
The average hourly rate of freelancers in Nuremberg, Germany who have used Fine-Tuning in their recent projects is 41 €, which corresponds to a daily rate of about 325 € based on an 8-hour working day.
Of the freelancers in Nuremberg, Germany who have used Fine-Tuning in their recent projects, 100% hold at least a Bachelor's degree and 100% hold at least a Master's degree.
On average, freelancers in Nuremberg, Germany who have used Fine-Tuning in their recent projects have 10 years of professional experience, with a single engagement typically lasting around 2.1 years.
The most common languages among freelancers in Nuremberg, Germany who have used Fine-Tuning in their recent projects are German (100%), English (100%), and Hindi (29%).
The most common industries among freelancers in Nuremberg, Germany who have used Fine-Tuning in their recent projects are Information Technology (86%), Education (43%), and Manufacturing (43%).
The most common business areas among freelancers in Nuremberg, Germany who have used Fine-Tuning in their recent projects are Product Development (100%), Research and Development (100%), and Information Technology (86%).
Main locations of FRATCH Experts, who have recently used Fine-Tuning
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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Munich Request a free demo
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Philipp Thomaschewski
FRATCH CEO
