Hugging Face Experts in Munich
, matched in minutes from over 15,000 CVsHire experts who fine-tune transformer models, build NLP and computer vision workflows, and deploy solutions through the Hugging Face Hub and Inference tools. FRATCH matches you quickly and precisely with vetted, available freelancers.
Meet FRATCH Experts in Munich, who have recently used Hugging Face
Thomas H.
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).
Andreas A.
Last position:
AI Consultant & Digital Architect at TeamIntel
- Governed multi-agent orchestration for regulated, EU-based companies – self-hostable, compliant with the EU AI Act and GDPR („by design“), BYOM (own models/GPU).
- Two-gate governance: agent deliberation + mandatory human approval, complete signed audit trail; graduated autonomy model („internal → autonomous per skill“).
- Verified knowledge graph („Company Brain“) with source evidence for each answer; proprietary orchestration framework (Virtual Team Framework).
- Industry solutions for financial services: compliance monitoring, invoice and contract review; hands-on development with LLMs (including Anthropic/Claude), agentic workflows, RAG.
- Development of the governance-focused multi-agent platform TeamIntel (see AI reference projects).
Siegfried-Thor B.
Last position:
Independent AI Risk Auditor & Software Architecture Reviewer at Confidential Client
Conducted an independent technical assessment of an AI-enabled software platform, combining AI risk auditing, cybersecurity analysis and software architecture review. Reviewed application components, APIs, data flows, LLM integrations, retrieval-augmented generation, multi-agent orchestration, authentication, tenant isolation, dependency vulnerabilities, AI governance and model lifecycle controls. Authored a technical audit report with a structured risk register, source-code evidence, regulatory references, corrective actions, AI system inventory, risk classification, remediation roadmap and Key Risk Indicators.
Technologies: TypeScript, JavaScript, React, Next.js, Node.js, REST APIs, MongoDB, Redis, AWS serverless services, OAuth 2.0 / OpenID Connect, LLM APIs, RAG, vector search, multi-agent orchestration, Model Context Protocol, C4 architecture modelling, threat modelling, NIST AI RMF, ISO/IEC 42001, ISO/IEC 23894, OWASP guidance, MITRE ATLAS
Narges D.
Last position:
Research Assistant at Hochschule München
Introduced an integrated approach for structural damage detection across concrete, steel, and glass using advanced technologies such as LiDAR and thermal imaging. Highlighted cross-material interactions to enhance diagnostics and enable predictive maintenance.
Developed an NLP-based medical note simplifier that transforms complex clinical instructions into plain, child-level English. Applied prompt engineering with Flan-T5 transformer models to extract patient-relevant actions and rephrase them into clear to-do items. Built dual Flask and Tornado backends with a printable web interface.
Martin R.
Last position:
Senior LLM Research Scientist at BYO Inc.
- Research and develop models for chatbots, NLP and LLMs (e.g. Llama, Qwen, OpenAI)
- Enhance chatbots with RAG, in-context learning
- Supervised fine-tuning (PEFT, LoRA), Huggingface or Unsloth
- Advanced training methods: Test-time training, (transductive) active learning, reinforcement learning
- High-throughput serving with vLLM
- Apply embedding models (e.g. SentenceTransformers), similarity/vector search or vector DB or ranking (e.g. LlamaIndex, Faiss, LangChain)
- Generate and filter synthetic data, clustering
- Detect hallucinations
- Evaluate chatbot models (Rouge, BLEU, F1-Score, Recall, Precision)
- Visualization of experiments (matplotlib)
Tezcan D.
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
Caner K.
Last position:
Synthetic Medical Dataset (MedGym) at MedTank
- Generated synthetic datasets for CXR, mammography, and distal radius fracture detection using GANs and diffusion, creating >50k synthetic images for benchmarking.
- Ensured GDPR-compliant workflows and reproducibility, enabling dataset adoption for internal validation and academic collaboration.
- Project highlighted in MedTank’s internal R&D showcase as a flagship synthetic data initiative.
Christian S.
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.
Martin M.
Last position:
Product Owner AI Learning Platform at B2B Tech Scale-Up
- Agile development of a multimodal analysis platform for training materials (video, audio, documents) using Scrum
- Extraction of context-relevant content based on user profiles & competency dimensions
- Personalized delivery of learning content to improve sales performance
- Close coordination with sales teams & stakeholders to validate features
- Use of Gemini, Whisper, Python & JavaScript, deployment on AWS, Perl for scripting data imports
- Integration into existing tools & CRM systems for smooth adoption
- Technologies used: Python, OpenAI, DB tech such as PostgreSQL, CI/CD for AirFlow DAGs, FastAPI
Nima N.
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
Nurbüke T.
Last position:
Working Student – Software Engineer at Rohde & Schwarz
- Developing software tools within the EICACS program (LDACS project) supporting secure avionics communication.
- Built Python-based automation and monitoring services to validate AI components under Trustable AI guidelines.
- Designed CI/CD and test pipelines improving reproducibility and reliability across teams.
Anton K.
Last position:
Head of Overall Technical Integration NSC / Hadoop Cloud Development at IABG
Head of overall technical integration NSC (National Secure Cloud, project with approx. 60 employees).
Technical integration of all subprojects into one product, definition of interfaces and basic components of a cloud including hardware, technical architecture of the IABG platform.
Development of a Cloud Management Platform (CMP) capable of creating private/mixed clouds of any complexity based on a textual description with one click or interactively.
CMP also includes the complete hardware management lifecycle.
Kubernetes, OpenStack and Hadoop are used as the foundation.
The management layer includes Harbor, Gitea, Longhorn, Keycloak, Rancher and Jenkins, which are configured automatically.
Private cloud can run any customer workloads, including a full Hadoop layer with HDFS, Spark, MapReduce, Mesos, HBase and around 20 additional ML/DL technologies.
Hadoop worker clusters can also be installed automatically without Kubernetes on bare metal or commodity hardware.
OpenStack with Nova, Neutron, Ironic, Swift, Cinder, Ceph.
Development of a Java application Rudi: SOAP, REST, containers, DB.
Technologies: Kubernetes (K3s, Rke2, Minikube, Harbor, Gitea, Jenkins, Longhorn, Keycloak, Rancher), OpenStack (Nova, Neutron, Keystone, Swift, Ceph, Cinder, Sahara, Magnum, Kayobe, Kolla, Bigrost, Ironic), Hadoop (HDFS, Ambari, Solr, Livy, Ranger, YARN, Tez, HBase, Kafka, Hive, Zookeeper, MapReduce, Spark, Oozie, Flink), virtualization (Kubernetes (K3S), VMware, Oracle), scripting (Ansible, Puppet, Juju, Shell, Groovy, Gradle, Maven).
Mohamed S.
Last position:
Machine Learning Engineer (Part Time) at E.ON Digital Technology
- Designed and implemented an advanced, agentic RAG pipeline using LangChain and LangGraph for structured data extraction from PDFs, utilizing tools, state management, and OpenAI LLMs (GPT-4) to improve accuracy and handle complex document structures.
- Developed a Google AI agent for extraction of structured information from PDF documents and deployed the agent on Vertex AI.
- Architected data pipelines using Azure Data Factory and Databricks to ingest data from Azure Blob Storage, process it with PySpark, and load it into Azure SQL Database via Linked Services.
- Containerized AI agents and services using Docker for consistent local development and deployment.
- Utilized PySpark and Dask for database querying in coordination with Azure Blob Storage and Document Storage.
- Created a ReAct agent that extracts structured data from PDF documents using tools and integrating Azure Document Intelligence.
- Contributed to the CPO invoices validation check project using Databricks to find existing CDRs and calculate total valid costs.
- Developed a conversational AI agent (chatbot) with a FastAPI backend, integrating RAG for precise tariff extraction and deployed the service using Azure Container Apps.
- Tools used: Azure, Azure OpenAI, Azure Document Intelligence, Azure Blob Storage, Google ADK, Google Cloud, Vertex AI, Gemini, Databricks, LangChain, LlamaIndex Ollama, Docker, PySpark, Azure SQL, Azure Data Factory, Azure AI Agent, Microsoft SQL Server
Vasco A.
Last position:
AI Research Intern – Generative AI at BMW AG
- Designed and implemented multi-modal entertainment toolchains that combine passenger input, vehicle context, large-language models (text-to-text and speech-to-speech) and image generation models to deliver more interactive and immersive in-car experiences.
- Built and orchestrated tools for LLM-based agents, covering session management, background task execution, dynamic user interactions and persistent application state.
- Investigated multi-agent orchestration frameworks for in-car environments, evaluating communication protocols and architectural strategies for coordinated and reliable agent behavior.
Thomas L.
Last position:
Consultant for AI-supported process automation at Lumiz
AI-supported automation of purchases on a printing company website, including the selection of delivery times, order options, ordering, payment and uploading print data from the Lumiz cloud.
Discover over 15,000 top freelancers
Statistics of experts using Hugging Face
Aggregated from the professional profiles of matched freelancers.
Experience
19 years (Germany: 12 years)
Position duration
1.4 years (Germany: 1.8 years)
Positions per freelancer
16 (Germany: 9)
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Manufacturing, Automotive
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
93% (Germany: 96%)
Master's degree or higher
86% (Germany: 79%)
Doctorate
29% (Germany: 14%)
Certifications per freelancer
2
Most common languages
English, German, Italian
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 Munich 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 Munich using Hugging Face
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.
Hugging Face 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 (93%)
- Manufacturing (67%)
- Automotive (60%)
- Healthcare (53%)
- Education (47%)
- Banking and Finance (47%)
- Insurance (47%)
- Government and Administration (40%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Hugging Face provides
Hugging Face is an open-source ecosystem for building, sharing and deploying machine learning models. Its Transformers library supports language, vision, audio and multimodal workloads, while the Hub provides a central place for models, datasets and demonstrations. Companies use it to move from experimentation to production more efficiently.
Models and deliverables
Professionals working with Hugging Face can adapt foundation models to a company’s data, product and quality requirements. Typical deliverables include:
- Fine-tuned text classification, extraction and generation models
- Retrieval-augmented generation and question-answering systems
- Image, speech and multimodal inference pipelines
- Evaluation workflows, model cards and dataset documentation
- Production APIs and internal AI applications
Ecosystem and tooling
The ecosystem extends beyond Transformers. Strong specialists may work with the Hugging Face Hub, Datasets, Tokenizers, Accelerate, PEFT, TRL and Diffusers, along with PyTorch or TensorFlow. They also understand experiment tracking, vector databases, containerization, cloud inference and model serving, so individual components fit into a reliable system.
When companies need specialists
Freelance expertise is useful when an internal team needs to validate an AI use case, adapt a model or reduce the gap between a notebook and a maintained service. It can also help when proprietary data requires careful preprocessing, when model output needs systematic evaluation, or when an existing solution must be optimized for latency, cost and privacy.
Munich delivery context
In Munich, Hugging Face work can support manufacturing, automotive, research, healthcare and enterprise software teams handling documents, technical knowledge, images or speech. A specialist may collaborate remotely with a local product group or work on site for workshops and data reviews. German and English communication can matter when requirements, domain material and user-facing outputs span both languages.
What strong professionals bring
The best professionals combine model knowledge with sound software practice. They define evaluation criteria before tuning, check data quality and licensing, document assumptions, and test failure cases rather than relying on impressive demos. They can explain trade-offs between an open model, a hosted API and a smaller task-specific model, then deliver reproducible pipelines that teams can operate after handover.
Frequently asked questions
Quick answers to the questions that come up most around Hugging Face.
Hugging Face is used to access, adapt, evaluate and deploy machine learning models for language, vision, audio and multimodal applications. Teams commonly use Transformers, the Hub and Datasets to create search, summarization, classification, document processing and generative AI features.
Hugging Face gives teams more choice over open models, fine-tuning, hosting and data handling than a single hosted API usually provides. Hosted APIs can be faster to start, while Hugging Face is often a better fit when customization, model portability or controlled deployment matters.
A strong Hugging Face specialist usually also understands Python, PyTorch, data preparation and evaluation design. Depending on the project, useful adjacent skills include retrieval systems, vector databases, cloud infrastructure, Docker, Kubernetes, MLOps and responsible AI practices.
The right level depends on the outcome, not just the model choice. A prototype may need experience with pipelines and the Hub, while fine-tuning, multilingual data, safety controls or production serving calls for a specialist who has handled the full lifecycle from dataset design to monitoring.
Yes. Hugging Face projects are often suitable for remote collaboration because code, datasets, model versions and evaluations can be shared digitally. On-site sessions in Munich can still help with domain workshops, sensitive data processes and alignment with product or research teams.
Ask a Hugging Face specialist to show how they define success, test edge cases and document the model and dataset. Look for reproducible training or inference steps, clear licensing checks, measured error analysis and a handover that explains how the service will be operated.
Hugging Face supports models and tooling that can be used for German and multilingual workloads, but results depend on the selected model, data quality and evaluation method. A capable specialist should test German terminology, compound words, dialect or domain language where relevant instead of assuming English results will transfer.
The Hugging Face Hub is a repository and collaboration service for models, datasets and demos. Transformers is the library used to load and work with many transformer-based models, so the Hub and Transformers often appear together but serve different purposes.
The average hourly rate of freelancers in Munich, Germany who have used Hugging Face in their recent projects is 89 €, which corresponds to a daily rate of about 712 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Hugging Face in their recent projects, 93% hold at least a Bachelor's degree, 86% hold at least a Master's degree, and 29% hold a doctorate.
On average, freelancers in Munich, Germany who have used Hugging Face in their recent projects have 19 years of professional experience, with a single engagement typically lasting around 1.4 years.
The most common languages among freelancers in Munich, Germany who have used Hugging Face in their recent projects are English (100%), German (93%), and Italian (27%).
The most common industries among freelancers in Munich, Germany who have used Hugging Face in their recent projects are Information Technology (93%), Manufacturing (67%), and Automotive (60%).
The most common business areas among freelancers in Munich, Germany who have used Hugging Face in their recent projects are Information Technology (100%), Product Development (100%), and Research and Development (73%).
Main locations of FRATCH Experts, who have recently used Hugging Face
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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