LlamaIndex Experts in Munich
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Meet FRATCH Experts in Munich, who have recently used LlamaIndex
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
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
Martin Ratajczak
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)
Oussama El Allam
Last position:
Head of R&D at eXagotec GmbH
- Spearheading multidisciplinary engineering teams in the development of next-generation medical devices
- Orchestrating research initiatives and technology roadmaps to deliver innovative medical solutions
- Overseeing R&D budget and managing project portfolios from concept through to commercialisation
- Establishing strategic collaborations with clinical partners for technology validation
Max Ritter
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 Saleh
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
15 years (Germany: 12 years)
Position duration
1.6 years (Germany: 2.2 years)
Positions per freelancer
13 (Germany: 8)
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Automotive, Education
Certification focus areas
Information Technology, Business Intelligence, Operations
Bachelor's degree or higher
86% (Germany: 96%)
Master's degree or higher
86% (Germany: 81%)
Doctorate
57% (Germany: 23%)
Certifications per freelancer
3 (Germany: 2)
Most common languages
German, English, Arabic
Speak two or more languages
100% (Germany: 93%)
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 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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
Retrieval apps
LlamaIndex is used to build retrieval-augmented applications that can answer questions from company data instead of only from a model’s training set. It helps specialists connect documents, databases, APIs, and vector stores into one search and answer flow.
What it handles
- Document loading and parsing
- Chunking and indexing
- Query engines and chat over data
- Multi-source retrieval and routing
This makes it a strong fit for internal knowledge tools, support assistants, research assistants, and workflow automation.
Ecosystem fit
LlamaIndex sits beside Python, embeddings, vector databases, and LLM providers such as OpenAI, Anthropic, or open source models. Strong professionals know how to combine it with data cleanup, metadata design, reranking, and evaluation so the system returns useful context, not noisy text.
When companies need help
Teams usually bring in freelance expertise when a prototype needs to become stable, when retrieval quality drops, or when data sources keep changing. In Munich, this often comes up in software, industrial, mobility, and enterprise settings where local teams need clear collaboration and English-friendly delivery.
What strong specialists do
Good LlamaIndex professionals do more than wire up a demo. They test retrieval behavior, choose the right index strategy, reduce hallucinations by improving context, and make the pipeline maintainable for the next team that touches it.
Related names and skills
LlamaIndex is still sometimes searched under its former name, GPT Index. The best experts also understand RAG design, prompt handling, vector search, and observability. That mix matters when a project needs accurate answers, traceable sources, and clean handover.
Frequently asked questions
Not sure where to start with LlamaIndex? These answers cover the essentials.
LlamaIndex is used to build applications that retrieve information from company data and turn it into answers, summaries, or actions. It is common in knowledge assistants, internal search, customer support flows, and document-heavy tools where the model must work with private sources.
LlamaIndex focuses strongly on data ingestion, indexing, retrieval, and query over your content. LangChain is broader and often used for orchestration across tools and agents, so many teams use one or the other depending on whether the main problem is data retrieval or workflow control.
Yes, LlamaIndex was formerly known as GPT Index. Many searchers still use the old name, especially when looking for older tutorials or examples, so a freelancer should understand both terms and know how the library evolved.
A strong LlamaIndex specialist should also know Python, embeddings, vector databases, prompt design, and basic evaluation methods. It also helps to understand document parsing, metadata, and how to debug retrieval quality when the wrong context is coming back.
A small proof of concept can be handled by one experienced LlamaIndex professional who knows the library and the target data sources. Production work usually needs someone who can handle ingestion, retrieval tuning, logging, and maintainable integration with the rest of the stack.
Yes, LlamaIndex projects are often well suited to remote work because most tasks are code, data, and system design. For Munich teams, that usually means remote collaboration with clear access to documents, sample queries, and regular reviews, while on-site sessions can help when data ownership or security questions are sensitive.
Look for someone who can explain index choice, retrieval trade-offs, and why a given pipeline fits your data. A good LlamaIndex professional will talk about source quality, chunking strategy, reranking, and evaluation, not just about getting a demo running.
A LlamaIndex expert should be able to deliver a working retrieval prototype, a clear ingestion pipeline, and documented query behavior. For larger work, expect support for source mapping, prompt and retrieval tuning, and handover notes that let your team keep the system stable.
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 817 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used LlamaIndex in their recent projects, 86% hold at least a Bachelor's degree, 86% hold at least a Master's degree, and 57% hold a doctorate.
On average, freelancers in Munich, Germany who have used LlamaIndex in their recent projects have 15 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 LlamaIndex in their recent projects are German (100%), English (100%), and Arabic (29%).
The most common industries among freelancers in Munich, Germany who have used LlamaIndex in their recent projects are Information Technology (86%), Automotive (43%), and Education (43%).
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 Research and Development (86%).
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 
Berlin Request a free demo
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Philipp Thomaschewski
FRATCH CEO
