PyTorch Experts in Nuremberg
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Meet FRATCH Experts in Nuremberg, who have recently used PyTorch
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
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.
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
Uddipan Basu Bir
Last position:
Research Team Member at Munich Music Labs, TUM
- Focused on exploring the intersection of Music and AI.
Musaib Parray
Last position:
Research Intern – Exploring Reasoning with Diffusion Models at Machine Learning and Perception group, FAU Erlangen-Nürnberg
- Investigating the equivalence between the Tiny Reasoning Model (TRM) and diffusion models for structured reasoning tasks such as Sudoku and maze solving.
- Exploring the reasoning and generative capabilities of diffusion models in symbolic problem-solving environments.
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
Kashyap Khunt
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.
Aqsa Younus
Last position:
Multilingual Translation Tool - NLP Project
- Integrated MarianMT (Marian Machine Translation) models to ensure high-quality neural machine translation (NMT).
- Managed model loading and tokenization via Hugging Face Transformers, optimizing for offline caching and reproducibility.
- Planned extensions: language auto-detection, batch translations, and streamlined GPU inference with PyTorch.
Discover over 15,000 top freelancers
Statistics of experts using PyTorch
Aggregated from the professional profiles of matched freelancers.
Experience
7 years (Germany: 12 years)
Position duration
1.2 years (Germany: 1.8 years)
Positions per freelancer
5 (Germany: 8)
Top business areas
Research and Development, Information Technology, Product Development
Top industries
Information Technology, Education, Manufacturing
Certification focus areas
Business Intelligence, Information Technology, Research and Development
Bachelor's degree or higher
100% (Germany: 98%)
Master's degree or higher
100% (Germany: 83%)
Certifications per freelancer
2
Most common languages
English, German, Hindi
Speak two or more languages
100% (Germany: 98%)
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 PyTorch
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
Core use
PyTorch is a deep learning framework for building and running neural networks in Python. Teams use it for computer vision, NLP, recommender systems, and time-series models. It is a common choice when experiments need to move into reliable production code.
What experts deliver
- Model training and fine-tuning
- Custom data pipelines and loaders
- Inference services and batch scoring
- Evaluation, debugging, and model iteration
Strong specialists also work with Torch, the older name many people still use when they mean PyTorch or Torch7.
Ecosystem fit
PyTorch rarely stands alone. Experts often use TorchVision for images, TorchAudio for speech, Hugging Face for transformer models, and PyTorch Lightning for cleaner training loops. They also connect models to CUDA, ONNX, and container-based deployment stacks.
When companies bring help
Teams usually look for freelance support when a model project needs to start quickly, when an internal team lacks deep learning experience, or when a prototype has to become maintainable code. In Nuremberg, that often means work that must fit mixed setups with local specialists and remote collaboration.
Signs you need one
- Training is unstable or too slow
- Metrics look good in testing but fail in real use
- Data preprocessing is inconsistent
- Deployment and inference are not defined
A strong professional writes clear training code, explains trade-offs, and knows how to move from notebook work to reproducible runs. They also understand performance, memory use, and how to keep experiments trackable.
Practical collaboration
PyTorch work can be done fully remote, but on-site sessions help when teams need close alignment on data, infrastructure, or product requirements. The best specialists document assumptions, keep model versions clean, and hand over code that other experts can maintain. That matters in fast-moving product teams and industrial settings alike.
Frequently asked questions
Questions about PyTorch? Start with the answers below.
PyTorch is used to build and train neural networks for tasks such as image recognition, text classification, forecasting, and recommendation. It is also used for fine-tuning pre-trained models and turning research code into production systems. Companies choose it when they need flexibility during model development.
PyTorch is often preferred for flexible experimentation and a more natural Python workflow. TensorFlow is still common in many production stacks, especially where teams already rely on its tooling. The better choice depends on your existing codebase, deployment path, and the specialists available.
PyTorch is the better fit when your project needs deep learning rather than classic machine learning. scikit-learn is strong for structured data, baseline models, and simpler pipelines, but it does not cover neural network work in the same way. Many teams use both side by side.
A strong PyTorch freelancer usually knows Python well, plus data preparation, model evaluation, and GPU-aware training. Useful adjacent skills include CUDA basics, Docker, Hugging Face, ONNX, and cloud deployment. For many projects, clean testing and reproducible runs matter as much as model design.
PyTorch projects that stay in notebooks can move fast, but production work needs someone who has shipped real training and inference code before. The more complex the data, the model, or the deployment setup, the more important that experience becomes. For regulated or business-critical use cases, choose a specialist who can explain failures clearly.
Yes, PyTorch work is often well suited to remote collaboration because most tasks happen in code, data pipelines, and experiment tracking. For teams in Nuremberg, hybrid work can help when model requirements are tied to local operations or domain experts. Clear documentation and regular review cycles are key either way.
Look for a PyTorch specialist who writes reproducible training code, understands metrics, and can explain why a model performs the way it does. Good signs are clean project structure, sensible use of TorchVision or other ecosystem tools, and a practical approach to deployment. Ask for examples that show both experimentation and production discipline.
People use PyTorch and Torch to mean the same thing in everyday conversation, but they are not identical names. Torch7 was the older Lua-based framework, while PyTorch is the modern Python-first system used today. A good specialist will know the history, but will build with current PyTorch APIs.
The average hourly rate of freelancers in Nuremberg, Germany who have used PyTorch in their recent projects is 42 €, which corresponds to a daily rate of about 336 € based on an 8-hour working day.
Of the freelancers in Nuremberg, Germany who have used PyTorch 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 PyTorch in their recent projects have 7 years of professional experience, with a single engagement typically lasting around 1.2 years.
The most common languages among freelancers in Nuremberg, Germany who have used PyTorch in their recent projects are English (100%), German (90%), and Hindi (30%).
The most common industries among freelancers in Nuremberg, Germany who have used PyTorch in their recent projects are Information Technology (90%), Education (60%), and Manufacturing (60%).
The most common business areas among freelancers in Nuremberg, Germany who have used PyTorch in their recent projects are Research and Development (100%), Information Technology (80%), and Product Development (80%).
Main locations of FRATCH Experts, who have recently used PyTorch
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 
Dresden Request a free demo
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Philipp Thomaschewski
FRATCH CEO
