Large Language Model Experts in Munich
matched in minutes from over 15,000 CVsHire experts who design retrieval-augmented generation systems, fine-tune language models and connect GPT or open-source LLMs to reliable business workflows. FRATCH finds vetted, available freelancers with a fast, precise AI match.
Meet FRATCH Experts in Munich, who have recently used Large Language Model
Karen M.
Last position:
Personal AI Engineering Project — Croky AI at Crocky AI
Product:
- Built a production-ready AI platform for generating brand-aware marketing images and videos from product data, user requirements, and uploaded media.
- Own the platform architecture, technical roadmap, API design, security, deployment workflow, operational reliability, and model-provider strategy.
- Developed the core platform in .NET and built supporting AI and workflow prototypes in Python, applying language-independent API contracts and structured interfaces between services and model providers.
- Implemented reliable background processing with RabbitMQ, persisted workflow state, idempotent handling, retries, failure recovery, logging, secure storage, authorization, and credit accounting.
- Made pragmatic build-versus-buy and model-routing decisions based on reliability, latency, cost, and maintainability rather than novelty.
Agent Orchestration & RAG Systems
- Built and compared agent workflows using Microsoft Agent Framework, LangGraph, and LangChain, including tool use, conditional routing, clarification steps, state management, and hand-offs between agents.
- Implemented reusable .NET components for agents, prompts, tools, model providers, structured responses, and retrieval with pyvector, making it easier to change AI providers without rewriting the core workflow.
Hachem S.
Last position:
Machine Learning & BI Lecturer at EU Business School
- Taught ML, BI, and Big Data to Master's students.
Tech: Python, Power BI, SQL, ML, SnowflakeDB, databricks, MS Fabric, MongoDB, LLM
Kristina S.
Last position:
Agile Transformation Coach – SAP Program (Freelance) at Sherpa X Digital Transformation SAP at Siemens
- Agile Transformation Coach within an SAP-driven End-to-End Lead-to-Cash program, supporting leadership and management teams in implementing and evolving the Sherpa Way of Working, strengthening Agile practices, role definitions and responsibility clarity (RACI), and delivery effectiveness
- Member of the leadership core team for the Way of Working, shaping and evolving agile operating models, challenging existing practices, and driving pragmatic, system-level improvements
- Conceptualized a Polarion-based Scrum Master dashboard as a single, role-based entry point for sprint status, dependencies, risks, and governance artefacts, reducing reporting overhead and improving transparency
- Provided targeted 1:1 coaching to the Master Scrum Master and Scrum Masters, strengthening leadership capability, role effectiveness, and support for team-specific challenges, including the redesign of Scrum Master syncs and collaboration formats
- Worked with teams and leadership on End-to-End Lead-to-Cash process analysis and documentation in SAP Signavio, supporting alignment, transparency, and a shared understanding of process expectations across teams
Michael N.
Last position:
Senior AI Engineer | Forward Deployed Engineer at Tiefbau
- Development of an AI-powered project organization tool for a civil engineering company that intelligently links project, task, tender, schedule, and document data through a knowledge graph.
- Implementation of AI features for document analysis, information extraction, context-based assistance, and voice-based data capture based on Microsoft Azure AI, reducing administrative effort, making information available faster, and supporting project teams in decision-making.
- Tech stack: Python, React, TypeScript, FastAPI, Claude Code, Codex, Graphify, PostgreSQL, Microsoft Azure AI Foundry, Azure OpenAI, Azure AI Speech, Azure AI Document Intelligence, Microsoft Graph, Microsoft Entra ID, Docker, Git, CI/CD.
Mirza K.
Last position:
Agentic Automation and a RAG system
- This project involved extraction of intelligence data to support report writing for a company that provides geopolitical, global, commercial intelligence. The data have been gathered from a number of resources (interview transcripts, online data, internal documents), and then a knowledge base has been build from it. This was the basis of a complex RAG system, that was evaluated against a golden dataset. Agents have been used to find out the contradicting intelligence, the statements supporting each other, and to store back the generated knowledge.
Used: Python, RAG, LangGraph, LangChain, deepeval, MCP
Fred H.
Last position:
Software Architect and Developer at Personal project
Recurring problem in my own AI-assisted projects: requirements analysis, use cases, and architecture decisions can be created quickly with AI support, but remain difficult to follow and scattered across Markdown files – knowledge is lost as soon as it is no longer in the context window. arknet turns requirements engineering and architecture knowledge into structured, verifiable data instead of plain text: requirements, use cases, and architecture decisions form a consistently linked knowledge graph, traceable from requirement to architecture decision – queryable by both people and AI agents. Technically based on RDF/OWL and a custom MCP server.
Result: Working MCP daemon, Docker image published automatically to GHCR, nine hexagonal modules, eleven ADRs (including an Open-Core licensing model). Requirements engineering and Ubiquitous Language hexagons are active. Public as a Community Edition under Apache-2.0 since 07/2026 (github.com/kogn-io/arknet), together with the Claude Code plugin and GHCR image; Open-Core model.
Label: Java, Maven, RDF, RDF4J, OWL, SPARQL, Model Context Protocol, Spring AI, Docker, GitHub, Git, Claude Code, Obsidian, DDD, Hexagonal Architecture, ArchUnit, JUnit, AssertJ, Interface Development, Software Architecture, Continuous Integration, Knowledge Management
Franz B.
Last position:
Product Development (AI) at Own initiative
AI telephone assistant platform
Claude Code, Google AI Studio, Python, LLM / Voice-AI, PostgreSQL
- Conception and hands-on development of an AI-supported telephone assistant platform (voice AI / LLM) – from idea and architecture to MVP/product.
- Built agentic workflows and full automations with Claude Code and Google AI Studio.
- Also delivered AI-supported work in client engagements: used Claude Code for governance documentation, requirement drafts, and automations.
Florian B.
Last position:
Project Management / PMO Consultant
PMO & Project Organization Blueprint for Restructuring
Renewable Energy / Solar Equipment
Technologies / Methods: PMO setup, KPI tracking, project organization, Jira, Confluence
- Developed measures to improve management control during a restruct...
Krisztina T.
Last position:
Head of Finance and Accounting at Segula Technology Services GmbH
- Led a 16-person finance and accounting organization during a restructuring phase; strengthened closing discipline, financial control, cash transparency and accountability.
- Supported cost programs with approximately €2 Mio. in annual savings; established regular KPI reviews for P&L, working capital, POC and cash topics.
- Responsible for monthly, quarterly and annual closing under HGB/IFRS, management/group reporting, audit, tax and GoBD compliance.
- Finance automation with clear business benefits: 13-week liquidity planning, cash flow reporting, P&L versus plan, variance analysis and bank reconciliation; reduced manual work by 40–60 %.
- Improved accounts payable processes through OCR-supported invoice processing and three-way matching with exception handling.
- Sparring partner to management on restructuring, liquidity, working capital, risks and process simplification.
Philipp T.
Last position:
Founder & CEO at FRATCH.IO
AI-native B2B SaaS for freelancer sourcing; DACH market.*
Enterprise partnerships across four industries: structured and closed multi-stakeholder deals with Telefónica (Telco), Emma Matratzen (Retail), Nürnberger Versicherungen and Flatex (Financial Services), Hubert Burda Media and Serviceplan Gruppe (Media).
Revenue and growth: scaled FRATCH from €0 to €3.8M annual GMV, with ~80% of revenue sourced from founder-led direct outreach and partner relationships.
Channel partnerships: sold FRATCH as a SaaS solution to recruiting firms (e.g., YER) — built the partner-enabled motion alongside direct enterprise sales.
Team build: scaled FRATCH from solo founder to a team of 7 across engineering, product design, operations, and supply outreach.
Proprietary network asset: onboarded 15,000+ freelancers as registered users — the proprietary DACH network powering FRATCH's matching.
Built and launched FRATCH GPT (fratch.io/gpt): a production conversational AI agent. Architected the full stack — LLM orchestration, embeddings, re-ranking — with hands-on involvement in technical design and execution.
GTM build: owned the full go-to-market stack — outbound, LinkedIn (organic + paid), content, and sales enablement.
Markus O.
Last position:
Lead E-Solution Architect & Senior Requirements Engineer at Zasterbot-Oracle
- Clarification of project objectives, scope and the target business vision for building the AI-based knowledge database.
- Derivation of the initial architecture and implementation strategy for the Zasterbot chatbot, including separation of the MVP and further development stages.
- Development of a target business vision for building a structured knowledge database and integrating a future chatbot.
- Derivation and prioritization of use cases for information retrieval and provision by the chatbot.
- Modeling of data structures and flows for the effective organization of the knowledge database on the Base44 platform.
- Design and implementation of data models for storing and linking relevant information.
- Development of processes for extracting, analyzing and preparing raw data for the knowledge database.
- Ensuring data consistency and quality as the basis for the future chatbot.
- Planning the integration of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) for precise and context-specific answers.
- Implementation of functions for analyzing and displaying data from the knowledge database.
- Use of the Base44 platform with JSON Schema-based entities and a flexible permission concept.
- Implementation of Deno functions for backend logic, event processing and connecting external APIs.
- Integration of OpenAI services for initial data analysis.
Giuseppe A.
Last position:
Embedded Software Development at Inheco
- AI Integration (LLM & RAG): Designed and built an internal intelligent RAG system (Retrieval-Augmented Generation) based on LLMs, n8n, and vector data to automatically analyze technical documents and error logs.
- Design & Implementation: Designed a robust RS-232/UART communication interface for an SBC-based embedded device used to control medical shaker systems.
- Architecture & Protocol Design: Built a highly maintainable software structure (OOP, SOLID) and defined resilient, hardware-level communication protocols, including multithreading and advanced error handling.
- Quality Assurance & DevOps: Automated testing with xUnit, ran integration tests directly on the hardware target, and maintained technical documentation 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
Sebastian O.
Last position:
Founder & Managing Director at OS-Cons GmbH
- Consulting across two integrated areas: Commercial Strategy (pricing, sales steering, marketing strategy, market expansion, margin management) and Operational Efficiency (process automation, AI integration, workflow design, last-mile automation).
- Development of custom SaaS solutions, explicitly tailored to the specific requirements and processes of each company.
- Delivery of AI training and change management workshops for managing directors and specialist departments, including AI competence training with a certificate of attendance under Art. 4 of the EU AI Act.
Lukas N.
Last position:
Senior Product Designer (Process & Workflows) at Streckenheld
- Designed role-based delivery assignment workflows, switchable between own fleet and partner carriers, with traceable status chains from „pending“ to „in delivery.“
- Designed AI-assisted route optimization, where dispatchers review drive-time-optimized route suggestions as drafts and apply them in one click.
- Designed a central planning interface for delivery and route management, bringing table view, map view, and route composition into a single workflow.
- Built interactive prototypes to align new product features early with stakeholders and engineering.
Key methods: AI-assisted Product Design, Workflow Design, Role-Based Workflows, Dashboard Design, Interaction Design, Prototyping, Logistics/Operations UX, Stakeholder Collaboration
Philipp G.
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
Discover over 15,000 top freelancers
Statistics of experts using Large Language Model
Aggregated from the professional profiles of matched freelancers.
Experience
16 years (Germany: 15 years)
Position duration
2.1 years (Germany: 2.9 years)
Positions per freelancer
11 (Germany: 10)
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Automotive, Professional Services
Certification focus areas
Information Technology, Project Management, Product Development
Bachelor's degree or higher
96% (Germany: 95%)
Master's degree or higher
79% (Germany: 70%)
Doctorate
15% (Germany: 13%)
Certifications per freelancer
2 (Germany: 3)
Most common languages
English, German, French
Speak two or more languages
98% (Germany: 97%)
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 Large Language Model
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.
Large Language Model 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 (94%)
- Automotive (48%)
- Professional Services (48%)
- Banking and Finance (45%)
- Manufacturing (43%)
- Retail (37%)
- Education (32%)
- Energy (31%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What LLMs do
Large Language Models process and generate human language from patterns learned across large text collections. Companies use them to create conversational assistants, document search, summarisation, classification and content workflows. The work spans model selection, prompt design, application logic and operational controls.
Core applications
LLM projects often support customer service, internal knowledge access and software teams. Common deliverables include:
- Retrieval-augmented generation over private documents
- Chat and voice assistants with controlled responses
- Summarisation, extraction and document classification
- Content workflows with review and approval steps
- Evaluation pipelines for accuracy, safety and consistency
Models and tooling
Professionals work with GPT models from OpenAI, Claude, Gemini and open-source models such as Llama. They connect model APIs with Python or TypeScript services, vector databases, embedding models and orchestration tools such as LangChain or LlamaIndex. Strong implementations also include structured outputs, function calling, caching and observability.
When expertise matters
Freelance specialists are useful when a team needs to move from a promising prototype to a dependable product. They can assess whether prompting, retrieval, fine-tuning or a conventional machine-learning approach fits the task. Munich companies may also value on-site workshops alongside remote delivery, especially when product, compliance and domain teams must align.
- Sensitive data needs clear access and retention controls
- Responses must be tested against realistic user questions
- Model costs, latency and fallback behaviour need ownership
- Existing systems require secure API and identity integration
Skills beyond prompts
Good LLM work combines language-model knowledge with backend engineering, data preparation and product design. Specialists understand tokenisation, embeddings, context limits, hallucination risks and model evaluation. They can build retrieval indexes, design robust instructions, protect personal data and expose useful feedback to improve the system.
Choosing strong professionals
Look for evidence of shipped LLM applications, not just prompt examples. A strong professional explains the intended user journey, source quality, evaluation method and failure handling before recommending a model. They distinguish a polished demonstration from a maintainable service and document assumptions so internal teams can operate the result.
Frequently asked questions
Need clarity? These are the questions we hear most often about Large Language Model.
A Large Language Model can generate, classify, summarise and transform text. Companies use LLMs for support assistants, enterprise search, document processing, drafting and workflow automation, usually with application rules and human review around the model.
An LLM works with flexible language inputs and outputs, while conventional software depends on explicit rules and conventional machine learning often targets a narrower prediction task. LLM projects still require normal software architecture, testing, security and data governance.
A GPT model may suit a project that needs a mature hosted API, while Claude, Gemini or an open-source model may be preferable for different context, integration, privacy or deployment needs. The right choice depends on the data, response quality, latency, operating model and evaluation results.
A Large Language Model specialist should usually understand backend services, APIs, data preparation, embeddings, vector search and cloud operations. Experience with identity, observability, evaluation, privacy and user experience is also valuable for production work.
The LLM scope matters more than a fixed experience label. A simple internal prototype may need focused model and integration knowledge, while a customer-facing system needs proven work with retrieval, evaluation, security, monitoring and failure handling.
Yes, Large Language Model work is often suitable for remote collaboration because model APIs, code repositories and evaluation tools are accessible online. On-site sessions in Munich can still help with domain discovery, data access decisions and workshops with German-speaking stakeholders.
Ask an LLM professional to explain the data sources, evaluation approach, safety controls and fallback behaviour behind a delivered system. Strong specialists can show how they measure useful answers, handle uncertainty and keep model changes from breaking the wider application.
With Large Language Models, retrieval-augmented generation is usually suited to answers grounded in changing private information. Fine-tuning is more relevant when the goal is a consistent style, format or task behaviour, and it does not replace access controls or reliable source data.
The average hourly rate of freelancers in Munich, Germany who have used Large Language Model in their recent projects is 96 €, which corresponds to a daily rate of about 770 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Large Language Model in their recent projects, 96% hold at least a Bachelor's degree, 79% hold at least a Master's degree, and 15% hold a doctorate.
On average, freelancers in Munich, Germany who have used Large Language Model in their recent projects have 16 years of professional experience, with a single engagement typically lasting around 2.1 years.
The most common languages among freelancers in Munich, Germany who have used Large Language Model in their recent projects are English (98%), German (93%), and French (19%).
The most common industries among freelancers in Munich, Germany who have used Large Language Model in their recent projects are Information Technology (94%), Automotive (48%), and Professional Services (48%).
The most common business areas among freelancers in Munich, Germany who have used Large Language Model in their recent projects are Information Technology (94%), Product Development (93%), and Business Intelligence (61%).
Main locations of FRATCH Experts, who have recently used Large Language Model
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 
Dusseldorf 
Leipzig 
Dortmund 
Essen 
Bremen 
Nuremberg Request a free demo
Get in touch with the FRATCH team and we will get back to you within 4 hours.
Would you rather directly get in touch?
We always have the time for a call or email!
Philipp Thomaschewski
FRATCH CEO
