Machine Learning Experts in Nuremberg
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Meet FRATCH Experts in Nuremberg, who have recently used Machine Learning
Sabine L.
Last position:
Communications consultant for crisis communication/issue management at AQIM – Association for Quality in Interim Management
Planning, execution & moderation of crisis communication and reputation management
AQIM is a non-profit organization based in Austria. An association of interim managers from Germany and Austria. Founded in December 2024. Multi-part training with workshops and consulting for preventive crisis communication, in cooperation with the founding members and those responsible for communication. To assess crisis potential for the market entry strategy and ahead of a digital campaign.
The project in a nutshell:
Consulting and training for the purpose of:
- Analysis of crisis potential as a new market entrant and its internet-based communication activities
- Planning and developing crisis communication with targeted measures
- Developing a crisis plan to minimize reputation risks in an emergency
- Conducting training and information sessions
Success:
- What started as a one-time consulting event for preparing an information campaign became a multi-part workshop on preventive crisis communication. This not only gave the organization expertise and confidence in the event of acute challenges, but also gave the participating interim managers new skills for their day-to-day work on assignments
- The workshop enabled those responsible to launch the information campaign that was being prepared at the time and planned for six months, as scheduled at the end of January 2026 on social media
- Careful scheduling and defining how to handle critical voices in terms of terminology and tone, as well as compliance rules, ensured a smooth start and run of a 3-month information campaign
David O.
Last position:
Machine Learning Researcher at Pattern Recognition Lab, FAU
- Fine-tuned and evaluated alternative backbone architectures for the AFFGAN handwriting style encoder to better capture writer-specific features, benchmarking against the original baseline architecture and replacing the legacy VGG19 architecture with a custom Transformer-based encoder.
- Extended the system beyond word-level synthesis to line-level handwriting generation, integrating and adapting an existing line-generation architecture to support full-sentence output.
- Deployed the handwriting generation pipeline as a web application, enabling users to select a writing style, input custom text, and instantly generate a corresponding handwriting image.
- Integrated TrOCR into a teacher–student distillation framework to evaluate and improve readability of generated handwriting, improving OCR accuracy by 20% over the baseline.
- 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.
Tech: PyTorch, Python, GANs, CNN, ViT, TrOCR, Streamlit, CUDA.
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.
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
Amir A.
Last position:
Master's Thesis at Friedrich-Alexander University
- Analysis of the role of marriage as an informal insurance in Germany using econometric methods (supervisor: Prof. Dr. Harald Tauchmann).
- Prepared and cleaned multiple panel datasets and extracted relevant variables to create a final panel dataset with over 36,000 observations (2006–2020, SOEP).
- Designed an IV model in Stata to examine the causal link between couple separation and health shocks (mental/physical).
- Conducted robustness checks to ensure stability and validity of the results.
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.
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.
Guino N.
Last position:
Senior Data Engineer at Infomotion
- Building a Data Analytics platform for Karl Storz
- Generic development of all ETL processes
- Preparing and providing data in Databricks Delta tables for reuse in Databricks Machine Learning
- Technologies & Tools: Azure Data Factory, CI/CD Pipeline with GitHub DevOps, Python, Azure Databricks (Unity Catalog), T-SQL
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.
Felix K.
Last position:
Senior Data Scientist at Novartis Pharma AG
- Led the implementation and validation of digital technology for real-world mobility assessment across clinical trials with high compliance rates
- Headed a cross-functional initiative that bridged clinical and data science teams, aligning digital technology integration with strategic goals
- Innovated algorithm development for sensor data analysis, leveraging time-series data to extract novel insights and improve predictive accuracy
- Designed and deployed predictive models using machine learning, integrating digital device data with clinical datasets
- Delivered strategic insights through the creation of dashboards and reports, ensuring effective data quality control and visualization for clinical trials
- Ensured reproducibility in data science workflows by establishing robust coding practices, comprehensive documentation, and version control
- Co-led exploratory statistical plans for digital biomarkers, advancing understanding and application of digital technologies in healthcare research
- Advised clinical teams as a subject-matter expert on digital technologies, fostering strategic integration and effective utilization in clinical trials
- Managed stakeholder engagement by collaborating with internal and external stakeholders, including contract research organizations and technology vendors
- Influenced decision-making by presenting analyses and findings to various management levels and stakeholders, emphasizing actionable insights and implications
- Disseminated knowledge through peer-reviewed publications
Andreas V.
Last position:
Senior Software Developer, Architect at ifm Solutions GmbH
- IoT platform to connect, transform and get actionable results
- Built a microservice observability solution to improve insights for developers and operations teams
- Made advanced performance optimizations in .NET code, databases and time series
- Provided architectural advice to developer teams on performance, scalability, reliability and code quality
- Evaluated and built PoCs for new technologies, including time series databases and messaging platforms
- Technologies: C#/F#/Rust, InfluxDB, ClickHouse, Prometheus, Grafana, k6, RabbitMQ, EMQX, Docker, Kubernetes, Azure AKS
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.
Musaib P.
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.
Jisa S.
Last position:
Electrical Design Engineer Trainee at STEP Global Corporate Solutions LLC
- Electrical system design in power distribution, lighting, and low-voltage systems
- Drafted and designed electrical layouts using AutoCAD Electrical
- Performed lighting simulation and analysis with Dialux
- Utilized Revit MEP in Electrical for building information modeling
- Conducted electrical estimation and takeoff using PlanSwift
- Estimated material and labor costs for electrical projects
Discover over 15,000 top freelancers
Statistics of experts using Machine Learning
Aggregated from the professional profiles of matched freelancers.
Experience
11 years (Germany: 14 years)
Position duration
1.9 years (Germany: 2.8 years)
Positions per freelancer
7 (Germany: 9)
Top business areas
Research and Development, Information Technology, Product Development
Top industries
Information Technology, Education, Automotive
Certification focus areas
Information Technology, Research and Development, Business Intelligence
Bachelor's degree or higher
100% (Germany: 97%)
Master's degree or higher
81% (Germany: 76%)
Doctorate
5% (Germany: 18%)
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 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Nuremberg are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows the share of experts charging within that range.
Average rates of experts in Nuremberg using Machine Learning
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 29 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Machine Learning 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 (52%)
- Automotive (48%)
- Manufacturing (48%)
- Banking and Finance (29%)
- Professional Services (29%)
- Energy (24%)
- Healthcare (24%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Machine Learning does
Machine Learning enables software to learn patterns from data and produce predictions, classifications or decisions without fixed rules for every case. It supports recommendation engines, demand forecasting, fraud detection, search, language processing, image analysis and intelligent automation. The work spans the full path from raw data to a monitored production model.
Models and methods
Professionals choose methods based on the data, business goal and cost of mistakes. They work with supervised and unsupervised learning, deep learning, natural language processing, computer vision, time-series forecasting and reinforcement learning. Strong practice includes feature engineering, model evaluation, validation, explainability and careful handling of bias.
Ecosystem and tooling
The Python ecosystem is central, with pandas and NumPy for data work, scikit-learn for classical models, and PyTorch or TensorFlow for neural networks. Specialists may also use Jupyter, MLflow, Hugging Face, Spark and cloud services from AWS, Google Cloud or Microsoft Azure. Production work connects models to APIs, data warehouses, containers and monitoring systems.
Where companies use it
- Predict customer demand, churn or equipment failures
- Classify documents, images, messages and support requests
- Recommend products, content or next actions
- Detect unusual transactions and operational risks
- Extract meaning from text, speech and business data
Machine Learning runs in manufacturing, logistics, healthcare, finance, retail and software products. In Nuremberg, local industrial and logistics environments can benefit from specialists who understand both operational data and the requirements of production systems.
When freelance expertise helps
- A proof of concept needs a sound path to production
- Internal teams need help selecting models or tools
- Existing predictions are inaccurate, slow or hard to explain
- Data pipelines and model monitoring are incomplete
Companies often bring in freelance expertise for a focused project, a technical review or temporary capacity. Remote collaboration works well when data access, documentation and ownership are clear. On-site workshops in Nuremberg can help when domain knowledge, equipment or stakeholders are difficult to reach remotely.
What strong specialists deliver
A strong professional connects statistical reasoning with practical software delivery. They clarify the target, establish a trustworthy data set, define useful evaluation criteria and compare a simple baseline with more complex approaches. They also document assumptions, protect sensitive data and make results understandable to the people using them.
Look for evidence of reproducible experiments, robust deployment and monitoring after launch, not only impressive model scores. The right specialist can explain trade-offs between accuracy, latency, cost, maintenance and fairness. They should also collaborate clearly in the language and working style your Nuremberg team requires.
Frequently asked questions
What clients ask us most about Machine Learning — answered in short.
Machine Learning is used to predict outcomes, classify information, detect anomalies and automate decisions based on data. Common applications include demand forecasting, recommendations, document processing, fraud detection, quality inspection and customer support.
Machine Learning learns patterns from examples, while rule-based software follows logic written directly by a specialist. It is useful when relationships are complex or change over time, but rules may be easier to audit when the decision process is simple and fixed.
A strong Machine Learning specialist often combines Python, statistics, data engineering and software development. Experience with SQL, cloud infrastructure, APIs, containerisation, experiment tracking and model monitoring is valuable for taking a model into reliable production.
The right Machine Learning experience depends on the project’s risk, data quality and production demands. A prototype may need focused modelling and validation, while a customer-facing system requires proven delivery across data pipelines, deployment, monitoring and responsible use.
Machine Learning projects often work well remotely because data, code and experiments can be shared through secure systems. On-site collaboration in Nuremberg can still help with manufacturing processes, physical devices, workshops or close access to domain specialists.
Before starting Machine Learning work, define access rules, data ownership, security controls and the intended use of the model. The specialist should be able to work with representative data, document transformations and explain how sensitive information is protected.
Ask a Machine Learning professional to explain the baseline, evaluation method, error patterns and expected behaviour outside the training data. Strong answers cover reproducibility, monitoring, explainability and the operational trade-offs behind the chosen approach.
Machine Learning is a field within artificial intelligence that uses data-driven methods to learn from examples. Deep learning is a specialised approach based on neural networks, while AI is the broader area that also includes reasoning, search, planning and other techniques.
The average hourly rate of freelancers in Nuremberg, Germany who have used Machine Learning in their recent projects is 61 €, which corresponds to a daily rate of about 489 € based on an 8-hour working day.
Of the freelancers in Nuremberg, Germany who have used Machine Learning in their recent projects, 100% hold at least a Bachelor's degree, 81% hold at least a Master's degree, and 5% hold a doctorate.
On average, freelancers in Nuremberg, Germany who have used Machine Learning 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 Machine Learning in their recent projects are English (100%), German (95%), and Hindi (24%).
The most common industries among freelancers in Nuremberg, Germany who have used Machine Learning in their recent projects are Information Technology (86%), Education (52%), and Automotive (48%).
The most common business areas among freelancers in Nuremberg, Germany who have used Machine Learning in their recent projects are Research and Development (86%), Information Technology (81%), and Product Development (76%).
Main locations of FRATCH Experts, who have recently used Machine Learning
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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FRATCH CEO
