PyTorch Experts in Munich
in minutes from over 15,000 CVs with the power of AI.Hire experts who build and train PyTorch models, tune Torch workflows, and ship reliable deep learning systems for vision, NLP, and forecasting. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Munich, who have recently used PyTorch
Michael Nelz
Last position:
Senior ML Engineer, AI Engineer at Lanxess AG
- Deployment and scaling of existing ML initiatives, including demand and cash flow forecasts.
- Building robust monitoring with mlflow for data stability, model performance, and drift detection, as well as implementing additional ML use cases.
- Further development of an Agentic AI chatbot for transparent and easy-to-understand model explanations.
Philipp Grunert
Last position:
Data Scientist & ML Engineer at Data-Science Factory GmbH
- Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
- Implementation of automated end-to-end cloud processes
- Development of LLM and NLP models
- Creation of interactive reports
- Support for national and international large corporations as well as medium-sized companies in implementing ML projects
Giuseppe Abrignani
Last position:
Embedded Software Developer at Inheco
- AI Integration (LLM & RAG): Design and build of an internal intelligent RAG system (Retrieval-Augmented Generation) based on LLMs, n8n, and vector data for the automated analysis of technical documents and error logs.
- Design & Implementation: Design of a robust RS-232/UART communication interface for an SBC-based embedded device to control medical shaker systems.
- Architecture & Protocol Design: Implementation of a highly maintainable software structure (OOP, SOLID) and definition of hardware-close, resilient communication protocols including multithreading and advanced error handling.
- Quality Assurance & DevOps: Test automation using xUnit, integration tests directly on the hardware target, and maintenance of technical documentation according to strict medical technology standards via Azure DevOps.
Label: C#, .NET, LLMs, RAG, n8n, RS-232, UART, Multithreading, async/await, xUnit, gRPC/protobuf, Blazor, MudBlazor, EF Core, Visual Studio 2026, Azure DevOps
Tezcan Dilshener
Last position:
Solution Architect / Project Manager at German Football Association
- Overall responsibility for the project lifecycle from scope definition to completion
- Close collaboration with platform teams, IT leaders, and external service providers
- Application of SAFe principles and structured sprint work
- Creation of a migration roadmap with clear milestones
- Monitoring of the lifecycle: onboarding, repository migration, replication of permissions, and system tests
- Visualization of the architecture with PlantUML and Gliffy as well as documentation in Confluence
- Regular status reports and running knowledge transfer sessions
Mirza Klimenta
Last position:
Agentic AI for a DeepResearch project at Freelance
- Created a multi-agentic system supported by a knowledge graph to automate drafting of research papers
- Used multiple experts (OpenAI models) collaborating during document drafting
- Extracted useful information from the knowledge graph
- Technologies: LangChain, LangGraph, Smolagents, LlamaIndex, dspy
- Infrastructure: Terraform and GitHub Actions (CI/CD) on AWS
- Deployed initial application as a Streamlit app
Thomas Hoefkens
Last position:
Senior MLOps, DevOps Engineer at Trianel Energy
- Build and operate an end-to-end MLOps platform on Azure ML and Kubernetes (Kubeflow) for the automated deployment, monitoring, and scaling of forecasting models (including Temporal Fusion Transformer, Informer, Autoformer).
- Implement CI/CD pipelines in Azure DevOps for the full ML lifecycle – from resource provisioning (Terraform), data transformation (Hugging Face Datasets, Pandas, PyTorch, CUDA cluster) through training and evaluation to model registry and endpoint deployment.
- Integrate MLflow for experiment tracking, model versioning, performance monitoring, and automated registration in the Azure Model Registry.
- Develop and containerize PyTorch training jobs (Azure Notebook, Jupyter Notebooks) for price and time series forecasting (PFC models) with automatic rollout via Azure ML Endpoints and REST/gRPC interfaces, Docker containerization, secured with OAuth 2.0.
- Set up monitoring and alerting mechanisms (Prometheus, MLflow Metrics), log centralization, and cost monitoring.
- Automate infrastructure provisioning and model deployment using Terraform, Helm, and Azure CLI; connect to existing market data systems and event pipelines.
- Migrate existing workloads and databases (IONOS → Azure, MongoDB) with integration into central MLOps workflows and internal networks.
- Extend the platform with LLM-based tools (LangChain, LangServe) to integrate GPT-based analysis modules into existing Spring Boot services for market anomaly detection and automated reports.
- Analyze and architect a software solution to process large volumes of data efficiently (>3000 messages/sec.) (market data store).
- Spring Boot / Java 21 container development with RabbitMQ for distributing stock market data via MongoDB (Kubernetes) with fast storage of data in Redis RMaps, deduplication, forwarding messages to Read Model queues, and building Read Models for UI display in MongoDB.
- Integration of RESTHeart to create a REST API for MongoDB.
- Build an Angular frontend to simplify data queries and master data maintenance.
- Agentic coding with remote and local LLMs (Claude Sonnet, Ollama Qwen) and MCP servers.
- Develop Python scripts for transforming and cleaning incoming stock market data (Pandas, scikit-learn).
André Howe
Last position:
Linux IT Admin at ReiserST
- Development and maintenance of IT architectures with embedded Linux systems.
- Designing, implementing, and optimizing backend applications and script-based solutions.
- Analyzing and resolving issues, including troubleshooting and user support.
- Developing and implementing security concepts for cloud solutions.
- Administering networks (DHCP, DNS, NTP, VPN).
- Technologies: Linux, PowerShell, Bash, Python, Ansible, Kubernetes, GitLab CI.
- Methods: Kanban.
Krithika Chand
Last position:
Professional Reorientation at Von Rundstedt
- Engaged in a structured career development program while strengthening German language proficiency (B1 level) and evaluating opportunities in ADAS/AD systems and requirements engineering.
Valery Khamenya
Last position:
Sr. Data Scientist & Engineer at Virtual Minds
- Development of high-performance ad distribution via auction
- Holistic (multi-campaign & multi-channel) advertisement placement optimization
- Algorithmic optimization for NP-Hard/NP-e
- Multiple Knapsack Problem with constraints
- Online estimation of parameters in stochastic environments
Tools: Python, R, Kotlin, MILP/SAT/CP Solvers, Pytorch, Pandas, Docker
Tobias Nawa
Last position:
Enterprise & Solutions Architect
- Building an independent enterprise IT setup — cloud strategy, network, AWS landing zone, security requirements, contract negotiations.
- Migration of all applications; avoiding high contractual penalties for the client.
- Onboarding and coordination o...
Thomas Langer
Last position:
Consultant for AI-driven process automation at Lumiz
AI-driven automation of purchasing on a printing company's website, including selecting delivery times, order options, ordering, payment, and uploading print data from the Lumiz Cloud.
René Welland
Last position:
Conference Operator at Brähler Systems GmbH
- Developed the iOS/Android Delegate App and the Conference Operator
- Updated and developed a user-friendly conference environment and real-time video streaming
- Optimized the overall conference experience by implementing customizable features for flexible setup
- Enhanced the efficiency and usability of conference technology, enabling a seamless workflow and improved participant interaction experience
Christian Schulz
Last position:
Data-Scientist/AI Engineer at The Marcom Engine GmbH & Co. KG
- Concept creation and implementing AI Agents in AWS Cloud
- Continuously alignment with stakeholders
- Collaborate with DevOps
- Technologies: Git, CI/CD (GitHub Actions), Python/ML, Streamlit, Deno/typescript, AWS SAM, AWS Bedrock, AWS Lambda, AWS Dynamo DB, AWS S3, AWS Event Bridge etc.
Andreas Blum
Last position:
Project Lead, Digital Transformation at SV Linde Tacherting e.V.
Researched, developed, and implemented comprehensive digital strategy to modernize and accelerate processes of sports club with approximately 1300 members.
System Architecture & Implementation: Conceived and set up central cost- and energy-efficient ARM-based server infrastructure.
Selected, installed, and configured open-source solutions for knowledge management, ticket booking, and member management.
Nima Nooshi
Last position:
Co founding LLM Engineer at LLM Ventures
- Co-founded an AI venture focused on building production-grade LLM applications and agentic systems
- Designed and implemented multi-agent AI workflows for financial and trading applications
- Developed LLM-powered copilot architectures for portfolio analysis, trade management, and personalized user coaching
- Built on-device and edge-deployed inference applications, optimizing models for low latency, privacy, and resource-constrained environments
- Led system architecture decisions across model selection, orchestration, state management, and deployment
Discover over 15,000 top freelancers
Statistics of experts using PyTorch
Aggregated from the professional profiles of matched freelancers.
Experience
14 years (Germany: 12 years)
Position duration
1.6 years (Germany: 1.8 years)
Positions per freelancer
11 (Germany: 8)
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Automotive, Manufacturing
Certification focus areas
Information Technology, Business Intelligence, Research and Development
Bachelor's degree or higher
98%
Master's degree or higher
93% (Germany: 83%)
Doctorate
24% (Germany: 20%)
Certifications per freelancer
2
Most common languages
English, German, Spanish
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 Munich 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 Munich 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
What PyTorch does
PyTorch is a Python framework for building and training neural networks. Teams use it for computer vision, natural language tasks, recommendation logic, and research-to-production model work. It is common under the name PyTorch, and many people also search for Torch or the torch library.
Typical deliverables
- Model prototypes and training loops
- Fine-tuned vision and NLP models
- Inference services and batch scoring jobs
- Evaluation pipelines and experiment tracking
- Export flows for deployment
Ecosystem and tools
Strong specialists work across the full stack around PyTorch: Python, CUDA, NumPy, Pandas, Hugging Face, Lightning, and deployment tools such as ONNX or TorchScript. They know how to move between notebooks, training code, and production services without losing reproducibility.
When companies bring in help
Companies usually look for freelance expertise when an internal team needs model development, faster iteration, or a clean path from prototype to release. In Munich, that often fits teams in automotive, industrial AI, robotics, and enterprise software that want on-site workshops or remote delivery with clear handover.
What strong specialists do well
Good PyTorch professionals write readable training code, manage data pipelines, debug shape and device issues, and measure model quality honestly. They also understand overfitting, transfer learning, distributed training, and deployment constraints, not just notebook experiments.
Hiring signals
Bring in help when you need to modernize an old Torch setup, stabilize training, integrate PyTorch with an API or batch system, or review an existing model before launch. The best experts can explain trade-offs clearly and work well with product, data, and infrastructure teams.
Frequently asked questions
Curious about PyTorch? Here are the answers that come up again and again.
PyTorch is used to build and train machine learning models, especially for computer vision, NLP, recommendation, and forecasting tasks. It is also used to turn research code into production-ready inference services, training pipelines, and model evaluation workflows.
PyTorch is often preferred for flexible model development and clear Python-first workflows. TensorFlow is still common in production stacks, but many teams choose PyTorch when they want faster experimentation, easier debugging, and smoother collaboration with research-oriented specialists.
PyTorch grew out of the older Torch ecosystem, so many searchers still use the word Torch when they mean the modern framework. In practice, Torch usually refers to the earlier Lua-based stack, while PyTorch is the current Python library most companies hire for.
A strong PyTorch specialist usually knows Python well and works comfortably with NumPy, Pandas, CUDA, and model-serving tools. For modern projects, experience with Hugging Face, experiment tracking, data versioning, and deployment basics matters a lot.
PyTorch work can start with a small prototype, but production projects need someone who has handled training stability, evaluation, and deployment concerns. If the model must run reliably in a business process, bring in a specialist early rather than after the first failed release.
Most PyTorch work can be done remotely because the code, data pipelines, and review cycles live in shared systems. On-site work in Munich is useful for early discovery, stakeholder workshops, and cases where data access, compliance, or lab hardware makes local collaboration easier.
A good PyTorch freelancer shows clean training code, solid evaluation methods, and clear reasoning about data, loss functions, and deployment limits. Look for evidence of shipped models, not just notebooks, and ask how they handle reproducibility, drift, and performance debugging.
PyTorch specialists in Munich are often a fit for computer vision, industrial inspection, robotics, and enterprise AI use cases. They also help teams that need model tuning, internal proof of concepts, or a migration from Torch to the modern PyTorch stack.
The average hourly rate of freelancers in Munich, Germany who have used PyTorch in their recent projects is 88 €, which corresponds to a daily rate of about 706 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used PyTorch in their recent projects, 98% hold at least a Bachelor's degree, 93% hold at least a Master's degree, and 24% hold a doctorate.
On average, freelancers in Munich, Germany who have used PyTorch in their recent projects have 14 years of professional experience, with a single engagement typically lasting around 1.6 years.
The most common languages among freelancers in Munich, Germany who have used PyTorch in their recent projects are English (100%), German (93%), and Spanish (20%).
The most common industries among freelancers in Munich, Germany who have used PyTorch in their recent projects are Information Technology (89%), Automotive (53%), and Manufacturing (51%).
The most common business areas among freelancers in Munich, Germany who have used PyTorch in their recent projects are Information Technology (96%), Product Development (93%), and Research and Development (76%).
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 
Cologne 
Frankfurt 
Stuttgart 
Dresden 
Nuremberg Request a free demo
Get in touch with the FRATCH team and we will get back to you within 4 hours.
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Philipp Thomaschewski
FRATCH CEO
