LlamaIndex Experts in Munich
in minutes from over 15,000 CVs with the power of AI.Hire experts who connect LLMs to private data, build retrieval workflows, and tune document pipelines for production, with fast, precise matching and vetted, available freelancers.
Meet FRATCH Experts in Munich, who have recently used LlamaIndex
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
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
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)
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
Max R.
Last position:
Cloud (AWS) | AI | DevOps | Data at Boehringer Ingelheim
- Architected and implemented an enterprise-grade AI Agent Platform leveraging Retrieval Augmented Generation (RAG) architecture to enhance clinical data insights.
- Established robust CI/CD pipelines for LLM applications using CDK and Jenkins, significantly reducing deployment times.
- Implemented comprehensive observability solutions that increased agent reliability across pharmaceutical environments.
- Designed scalable AI workflows with advanced orchestration that optimized context handling for enterprise data sources.
- Technologies: AI Agents (LangChain, LangGraph, Bedrock, Smolagents, Streamlit); LLM Operations (Tracing, Testing, Evaluation, LangSmith, LangFuse); Infrastructure-As-Code (AWS CDK, Terraform, Typescript, Jenkins); Vectors, Embeddings, RAG (OpenSearch, pgvector, PDF Extraction)
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
Discover over 15,000 top freelancers
Statistics of experts using LlamaIndex
Aggregated from the professional profiles of matched freelancers.
Experience
16 years (Germany: 13 years)
Position duration
1.5 years (Germany: 2.3 years)
Positions per freelancer
14 (Germany: 8)
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Automotive, Education
Certification focus areas
Information Technology, Business Intelligence, Operations
Bachelor's degree or higher
100%
Master's degree or higher
100% (Germany: 85%)
Doctorate
67% (Germany: 22%)
Certifications per freelancer
4 (Germany: 2)
Most common languages
German, English, Arabic
Speak two or more languages
100% (Germany: 93%)
Based on our profile pool as of 19 Sep 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 LlamaIndex
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 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
LlamaIndex 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 (100%)
- Automotive (50%)
- Education (50%)
- Banking and Finance (50%)
- Transportation (50%)
- Media and Entertainment (50%)
- Professional Services (50%)
- Energy (33%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What it is
LlamaIndex is a framework for connecting large language models to your own data. Teams use it to build RAG systems, document search, question answering, and agent workflows that need reliable access to files, databases, and APIs. It started as GPT Index, which is still how some specialists refer to it.
Core building blocks
- Load data from PDFs, web pages, databases, and cloud storage
- Chunk and index content for retrieval
- Add query engines, rerankers, and response synthesis
- Connect to vector databases and LLM providers
- Orchestrate tools, agents, and custom workflows
When companies bring in help
Companies usually look for freelance experts when a proof of concept has to become a stable internal search or support system. That often means fixing retrieval quality, reducing bad answers, or aligning the framework with existing data access rules. In Munich, this is common in software, industrial, and enterprise teams that need German and English content handled cleanly.
Typical project work
A strong LlamaIndex specialist can design document ingestion, build a knowledge assistant, or wire the framework into an app backend. They also handle evaluation, prompt shaping, source citation, and the handoff between retrievers, embeddings, and the model layer. Common deliverables include internal assistants, analyst tools, and customer support search.
What strong experts know
Strong professionals understand more than the library surface. They know how to structure content, choose chunking strategies, compare retrieval options, and debug why answers miss the right source. They also work well with Python, vector databases, APIs, cloud services, and the broader LLM stack.
Why this matters in Munich
Munich teams often need specialists who can work with product, data, and security stakeholders at the same time. Some projects are on-site or hybrid because they touch internal knowledge bases and sensitive documents. Others run fully remote when the data pipelines and review process are already clear.
Frequently asked questions
Not sure where to start with LlamaIndex? These answers cover the essentials.
LlamaIndex is used to connect LLMs to private or live data so the model can answer with context, not just general training. Companies use it for document search, internal knowledge assistants, support tools, and agent workflows that need retrieval from files, APIs, or databases.
LlamaIndex is often chosen when the main problem is data ingestion, indexing, and retrieval over company content. LangChain is broader and leans more toward orchestration and chaining components, so teams sometimes use both together or pick one based on the architecture they need.
Yes, LlamaIndex was previously known as GPT Index. Some freelancers and older project notes still use the former name, so it helps if your search or brief mentions both. The core idea is the same: make private data usable by an LLM.
A strong LlamaIndex specialist usually knows Python well and is comfortable with embeddings, vector databases, and API integration. Experience with prompt design, retrieval evaluation, cloud services, and document parsing is also useful because most projects sit inside a larger LLM stack.
A simple proof of concept may only need a LlamaIndex expert who can wire documents into a basic query flow. Production work needs someone who has handled retrieval quality, metadata design, access control, and failure cases such as stale sources or weak citations.
Yes, LlamaIndex work is often done remotely because the code, data pipeline, and review cycle can be shared in standard development tools. Munich teams sometimes prefer on-site sessions at the start when the project involves sensitive internal content, language requirements, or stakeholder alignment.
Look for clear examples of how the LlamaIndex freelancer improved retrieval, reduced hallucinations, or added source traceability. Good experts can explain chunking choices, indexing strategy, and why one retriever or vector setup was better than another for the use case.
With LlamaIndex, weak ingestion or poor chunking can lead to irrelevant answers, missing sources, and slow retrieval. A good specialist should be able to show how they test query quality, handle updates to the source data, and keep the system stable as content grows.
The average hourly rate of freelancers in Munich, Germany who have used LlamaIndex in their recent projects is 102 €, which corresponds to a daily rate of about 820 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used LlamaIndex in their recent projects, 100% hold at least a Bachelor's degree, 100% hold at least a Master's degree, and 67% hold a doctorate.
On average, freelancers in Munich, Germany who have used LlamaIndex in their recent projects have 16 years of professional experience, with a single engagement typically lasting around 1.5 years.
The most common languages among freelancers in Munich, Germany who have used LlamaIndex in their recent projects are German (100%), English (100%), and Arabic (17%).
The most common industries among freelancers in Munich, Germany who have used LlamaIndex in their recent projects are Information Technology (100%), Automotive (50%), and Education (50%).
The most common business areas among freelancers in Munich, Germany who have used LlamaIndex in their recent projects are Information Technology (100%), Product Development (100%), and Business Intelligence (83%).
Main locations of FRATCH Experts, who have recently used LlamaIndex
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 Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg 
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Philipp Thomaschewski
FRATCH CEO
