PyTorch Experts in Frankfurt
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Meet FRATCH Experts in Frankfurt, who have recently used PyTorch
Bardiya B.
Last position:
Data Scientist at Rewe Digital GmbH
Statistical Forecasting Algorithm
- Improvement of an statistical probabilistic forecasting algorithm for sales + evaluation
- Migration from R/On-premise to Python/Snowflake
- Productionalization on Snowflake in cooperation with data engineers & DevOps
Monitoring Dashboard
- Data engineering for preparation & provisioning of necessary data/resources on Snowflake
- Development & deployment of a Streamlit dashboard in Snowflake
ML-based Probabilistic Forecasting on Vertex AI
- Development of a ML-based probabilistic forecasting algorithm from scratch
- Implementation of MLOps pipeline in Kubeflow on Google Cloud Vertex AI
Tech Stack: Python, Snowflake/Snowpark, R, Streamlit, Gitlab/Gitlab CICD, Terraform, Google Cloud, Vertex AI (aiplatform SDK, gcloud CLI, feature store, model registry, etc), kubeflow
Harsh Vardhan A.
Last position:
System and Process Integrator 2 at Audi AG
Spearheaded the development and deployment of a Generative AI solution tailored for the automotive industry focusing on improving customer experience through AI-driven innovations.
Conducted in-depth market research to understand unique challenges and opportunities within the automotive sector by analyzing industry trends, customer pain points, and competitive offerings to inform the product strategy.
Formulated a strategic vision for the Generative AI solution targeting personalized customer experiences, aligned product vision with the company’s long-term goals and automotive market demands.
Enhanced customer satisfaction by introducing personalized AI-driven features, achieving a 15% increase in customer engagement and loyalty.
Attended and represented Audi AG on a group-wide level in workshops for AI strategy for customer experience.
Leveraged knowledge of recurrent neural networks and transformer architecture.
Utilized GPT-3 generative AI frameworks.
Employed TensorFlow and PyTorch for machine learning.
Used Tableau from Salesforce for data analysis and visualization.
Served as solution manager for the Business Architecture team.
Collaborated with business stakeholders within Audi OEM to gather requirements for CRM strategy including marketing department, CRM heads across countries, VW group brands and CARIAD SE.
Represented Audi AG in CRM strategy workshops held in different countries.
Discussed CRM strategy with head of CRM and Data based on workshop outcomes.
Conducted business analysis on gathered market data to improve customer experience.
Planned and launched marketing campaigns such as welcome mailing, license renewal reminders, Audi Progress Circle and Black Friday campaigns.
Managed project budget.
Acted as solution manager for the ONE.CRM team at CARIAD SE on loan from Audi AG.
Collaborated with business owners of VW group brands to develop a central solution.
Represented CARIAD SE in CRM strategy workshops in Spain, France and Italy.
Discussed CRM strategy with head of CRM at CARIAD SE based on workshop outcomes.
Conducted business analysis on market and brand data to improve customer experience.
Planned and delivered campaign capabilities from template to brands such as welcome mailing for Audi AG, SEAT and SKODA.
Managed project budget together with head of CRM.
Kevin G.
Last position:
AI Strategy & Governance / Freelancer at Al Gambit
- Architect AI strategies and smart business processes for companies implementing AI initiatives.
- Focus on pragmatic and trustworthy AI integration delivering tangible operational value.
Anton R.
Last position:
AI-Engineer at Publicly traded company, industrial safety technology
- Designed and implemented the agent-based AI architecture for a company-wide platform to securely deploy LLM-based agents
- Designed and implemented end-to-end RAG pipelines from multiple sources: document preprocessing, chunking strategies for different document types, embeddings, retrieval with re-ranking, and robust prompt orchestration
- Developed a modular context engineering framework with skill architecture, context isolation, and dynamic resource management; human-in-the-loop control for enterprise tool integrations
- Built the CI/CD pipeline, testing strategy, tracing on the software side as well as automated LLM and agent evaluations, red team testing and tracing, and handed over to a reproducible production environment (ISO27001 and SOC2 compliant)
Ahsan J.
Last position:
Data Analytics Developer at Level Next Productions
- Built Power BI dashboards and enabled data-driven strategies across digital platforms
Tan P.
Last position:
DevOps Engineer in the DevOps Team at Rise-World
- Implementation of specified DevOps solutions to automate infrastructure (Terraform, Bicep, CloudFormation, Ansible) on-premises datacenter (Ovirt, Proxmox, Ceph Cluster, MinIO) and private cloud.
- Administration, configuration and implementation of CI/CD DevOps pipelines (GitLab, GitFlow) to support development process (Artifactory, Prometheus, Istio, service mesh, Helm Chart, OpenShift (Red Hat Enterprise) / Kubernetes cluster), Red Hat Satellite.
- Administration, setup, monitoring and patching of Linux infrastructure based on Red Hat Enterprise for Dev, Test and QA.
- Use of Scrum and Kanban methods.
- Administration, configuration and implementation of security standards for deploying on Dev, Test, QA and Prod stages of the new ePA applications.
- Development of new plugins and add-ons needed on current infrastructure.
- Database support.
- Data analytics support (Python, Spark, Pandas, Power BI, Splunk Enterprise).
- Implementation of best practices for DevSecOps and BizDevOps using GitOps (ArgoCD), Streamlit framework, Semaphore Ansible UI.
- Configuration and testing of iperf, uperf, sysbench using benchmark-operator for external source data and IoT/MDM devices, creating reports via ELK / OpenSearch.
- Building a new Databricks platform to collect and analyze big data from different sources and IoT devices into Hadoop framework (Python, Pandas, PySpark, Power BI, Apache Airflow).
- Building backend data aggregation and processing to automate configuration deployment between different OpenShift clusters and big data framework (Python, Pandas, PySpark, Apache Spark, PostgreSQL, Django 2, Ansible Automation, Jira JSM).
- Building a new ML pipeline platform using Kubeflow, TensorFlow, KServe.
- Data extraction, transformation and loading from different data sources including structured and unstructured data to analytic DWH / big data cluster using Python, Pandas, Polars, Power BI, Django backend and PostgreSQL.
- Setup of new DevOps Test and QA HashiCorp Vault cluster for PKI and IAM.
- Configuration and testing of automated patching based on CVSS score, SIEM-integrated CVEs.
- Use of Nexpose and InsightVM to scan vulnerability events in network, host, container and application.
- Design and implementation of secure and scalable AWS architectures including VPC, EC2, S3, RDS and Route53 and similar setups on Azure and GCP.
- Automated system provisioning and deployment using CloudFormation templates.
- Configuration of IAM roles, policies and permissions to ensure secure access control.
- Patch management, backup automation and disaster recovery setup on AWS infrastructure.
- Monitoring and optimization of system performance using AWS CloudWatch and AWS Trusted Advisor.
- Support of VMware services (vSphere, Aria, Horizon) and the virtual desktop environment.
- Development and maintenance of CI/CD pipelines using Jenkins, GitLab CI/CD and AWS CodePipeline with interface to Nutanix.
- Configuration of AWS CloudWatch to monitor application performance and system events.
- Planning and execution of migration of on-premises applications to AWS cloud platforms.
- Deployment of containerized applications using Docker and Kubernetes in AWS environments.
- Deployment of internal software packages between availability zones using AWS CodeDeploy.
- Building and deploying ML models using Scikit-learn, XGBoost and Spark MLlib including hyperparameter tuning, model evaluation and production deployment.
Jens D.
Last position:
Product Owner & Senior Data Scientist at Legal Tech
- Led an international team of six developers in a Scrum environment
- Defined strategic goals for the project in coordination with stakeholders and the development team
- Prompt engineering for language models to improve the accuracy and relevance of generated responses
- Implemented LangChain components for a RAG chatbot to answer legal questions
- Technologies: GPT-4, LangChain, Python (Pandas, sklearn, streamlit), Docker, GitLab, ChromaDB
Discover over 15,000 top freelancers
Statistics of experts using PyTorch
Aggregated from the professional profiles of matched freelancers.
Experience
12 years
Position duration
1.8 years (Germany: 1.9 years)
Positions per freelancer
11 (Germany: 8)
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Automotive, Banking and Finance
Certification focus areas
Business Intelligence, Information Technology, Project Management
Bachelor's degree or higher
86% (Germany: 99%)
Master's degree or higher
71% (Germany: 84%)
Doctorate
14% (Germany: 19%)
Certifications per freelancer
9 (Germany: 2)
Most common languages
German, English, Spanish
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 Frankfurt 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 Frankfurt 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 29 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
PyTorch 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%)
- Automotive (57%)
- Banking and Finance (57%)
- Healthcare (43%)
- Professional Services (43%)
- Retail (43%)
- Transportation (29%)
- Manufacturing (29%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Deep learning foundation
PyTorch is an open-source machine learning framework used to create, train and deploy deep learning models. Its tensor operations, automatic differentiation and dynamic computation graphs support rapid experimentation as well as production systems. Teams use it for image analysis, language models, recommendation engines, forecasting and generative AI.
Model development
PyTorch specialists work across the full model lifecycle, from preparing datasets to evaluating and optimizing trained models. They select architectures, define loss functions, build training loops and manage validation. Strong work includes reproducible experiments, clear metrics and safeguards against overfitting.
- Design convolutional, transformer and sequence models
- Prepare datasets, augmentation and data loaders
- Fine-tune pretrained models for domain-specific tasks
- Track experiments and compare model versions
Ecosystem and tooling
The ecosystem includes TorchVision, TorchText, TorchAudio and TorchData for common data domains. Professionals may also use Hugging Face Transformers, ONNX, CUDA, Weights & Biases, MLflow and distributed training tools. TorchScript and TorchServe can help turn research code into services, while Python remains the main language around the framework.
Production use cases
Companies bring in PyTorch expertise for visual inspection, document understanding, speech processing, fraud detection, personalization and scientific computing. In Frankfurt, specialists may support finance, logistics, manufacturing, healthcare or research teams, working on-site, remotely or in a hybrid setup. German and English collaboration can both matter in local projects.
- Connect models to APIs, data pipelines and business applications
- Optimize inference with GPUs, batching and quantization
- Package deployments for cloud or on-premises environments
- Monitor model quality, latency and data drift
When to hire specialists
Freelance expertise is useful when an internal team needs to validate an AI idea, replace a fragile prototype or prepare a model for reliable operation. It also helps when GPU workloads, distributed training or unfamiliar research code slow delivery. Define the target outcome, available data, deployment environment and compliance constraints before the engagement starts.
What strong experts bring
Good PyTorch professionals combine mathematical understanding with practical software skills. They can explain why a model works, identify weak data or evaluation design, and keep training reproducible. Look for experience with testing, version control, containerization, observability and responsible handling of sensitive data. Clear documentation and a handover plan distinguish durable delivery from a one-off experiment.
Frequently asked questions
Curious about PyTorch? Here are the answers that come up again and again.
PyTorch is used to build and train deep learning models for computer vision, natural language processing, speech, forecasting and generative AI. It supports both research experimentation and production inference when paired with suitable deployment tooling.
PyTorch and TensorFlow both support deep learning, GPU acceleration and production deployment. PyTorch is often valued for its Python-first workflow and flexible experimentation, while the right choice depends on the existing stack, team skills, deployment target and model requirements.
A strong PyTorch specialist usually understands Python, SQL, data preparation and model evaluation. Useful adjacent skills include CUDA, cloud infrastructure, Docker, Kubernetes, Hugging Face Transformers, MLOps and API design.
The right level depends on the scope. A proof of concept may need strong model and data skills, while a production system also requires experience with testing, security, deployment, monitoring and integration into existing applications.
Yes. PyTorch work is well suited to remote collaboration when datasets, GPU environments and repositories are accessible securely. Frankfurt teams should agree on communication routines, documentation standards and whether German, English or both are needed.
PyTorch is a strong general choice for flexible model development and broad ecosystem support. JAX may suit teams focused on functional programming, compiler-driven optimization or specialized numerical workloads, so the decision should follow the model and infrastructure needs.
Ask the specialist to explain the data pipeline, baseline, evaluation method and failure cases. Review whether PyTorch code is reproducible, tested and documented, and check how the model will be monitored after deployment rather than judging only its headline metric.
A PyTorch freelancer should expect collaboration with data, product and software professionals, not only machine learning specialists. Clear interfaces, experiment tracking, realistic compute planning and early agreement on ownership of datasets and models help projects move smoothly.
The average hourly rate of freelancers in Frankfurt, Germany who have used PyTorch in their recent projects is 101 €, which corresponds to a daily rate of about 811 € based on an 8-hour working day.
Of the freelancers in Frankfurt, Germany who have used PyTorch in their recent projects, 86% hold at least a Bachelor's degree, 71% hold at least a Master's degree, and 14% hold a doctorate.
On average, freelancers in Frankfurt, Germany who have used PyTorch in their recent projects have 12 years of professional experience, with a single engagement typically lasting around 1.8 years.
The most common languages among freelancers in Frankfurt, Germany who have used PyTorch in their recent projects are German (100%), English (100%), and Spanish (14%).
The most common industries among freelancers in Frankfurt, Germany who have used PyTorch in their recent projects are Information Technology (86%), Automotive (57%), and Banking and Finance (57%).
The most common business areas among freelancers in Frankfurt, Germany who have used PyTorch in their recent projects are Information Technology (100%), Product Development (86%), and Business Intelligence (57%).
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 
Dusseldorf 
Essen 
Nuremberg Countries:
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Philipp Thomaschewski
FRATCH CEO
