LlamaIndex Experts in Germany
, matched quickly with vetted freelance professionalsHire experts who connect language models to private data, design retrieval-augmented generation pipelines and ship production-ready document workflows. FRATCH matches you precisely with vetted, available freelancers in minutes.
Meet FRATCH Experts in Germany, 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
Stanley A.
Last position:
Senior AI Engineer & Technical Lead at Independent / Freelance
- TrendReel, production LLM agent and RAG system (Python, LangChain, OpenAI, Groq/Llama 3, Claude, FastAPI, Kubernetes, PostgreSQL).
- Designed and built a production multi-step LLM agent system: a script generation agent with a per-platform psychology database, 7 viral narrative frameworks, and structured quality scoring, switching between Claude and Groq backends in real time based on output metrics.
- Implemented multi-provider LLM routing (Claude primary, Groq/Llama 3 fallback) with priority-chain failover and quality-based provider switching, achieving 95% inference cost reduction while holding measurable quality thresholds.
- Built an advanced RAG-style retrieval pipeline with per-platform knowledge bases, semantic content matching, and structured output evaluation across 7 decision frameworks, directly analogous to multi-tenant context-based reasoning for enterprise document workflows.
- BrainyAI, adaptive AI learning platform (Python, LangChain, Groq Llama 3.3-70B, OpenAI, Next.js, Supabase, Redis).
- Integrated Groq Llama 3.3-70B with education-level-aware prompting, dynamically adjusting vocabulary depth, citation complexity, and reasoning style across four student proficiency tiers.
- Nexus Prime, multi-tenant SaaS platform for marketing and growth automation (25 modules, 99 backend routers, 153 frontend files).
- Built a 25-module, 99-router multi-tenant SaaS platform covering ad remix, affiliates, WhatsApp inbox, email, and cart recovery, serving four subscription tiers from $199 to $1,999 per month with integrated Stripe, Paystack, and Flutterwave billing.
- AI Video Surveillance Platform, multi-tenant edge and cloud computer vision system currently in active client pitch.
- Designed a multi-tenant AI video surveillance platform combining edge YOLO26 inference on NVIDIA Jetson Orin NX boxes with a central GKE cloud layer (Postgres, Pub/Sub, ClickHouse, R2, Keycloak) for event storage, dashboards, alerting, and multi-tenancy.
Vishnu V.
Last position:
Senior Software Architect at Roche Diagnostics Automation Solutions
- Own the software system architecture for laboratory automation products; specify interfaces across software, middleware, hardware and motor control in a regulated IVD environment.
- Led architecture evaluations and proof-of-concepts for integrating AI capabilities (anomaly detection, predictive maintenance) into lab automation under medical-device quality standards.
- Introduced GenAI-assisted development tools across the team, improving productivity and code review quality.
- Communicate architecture decisions to product and project management; coordinate research and improvement projects with system, electronics and external partners.
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
Haseeb Z.
Last position:
Senior Data Scientist at WPP MEDIA
- Designed and deployed enterprise Retrieval-Augmented Generation (RAG) applications using LangChain, LangGraph, vector databases, embeddings, and open-source LLMs served through vLLM on GCP GPU infrastructure.
- Built agentic AI workflows using LangGraph with planning, reasoning, tool execution, persistent memory, session management, and Human-in-the-Loop approval mechanisms.
- Developed LLM-powered automation systems integrating BigQuery, SQL pipelines, and external advertising APIs including Meta, TikTok, Amazon, Snapchat, Google, and Pinterest, reducing manual operational workflows.
- Architected multi-agent AI systems for enterprise analytics and decision-support workflows, enabling autonomous task execution and intelligent data interactions.
- Implemented retrieval optimization strategies including multi-retriever architectures, semantic search, context optimization, and query improvement techniques, improving response relevance by approximately 40%.
- Engineered structured prompting strategies, function-calling schemas, and validation workflows to improve reliability of multi-step LLM applications.
- Designed scalable AI services using Python, FastAPI, Cloud Run, Pub/Sub, BigQuery, Docker, and cloud-native deployment architectures.
Partha N.
Last position:
AI Software Developer at Fraunhofer IIS
- Built a custom AI chatbot for an e-commerce client using GPT-4 and LangChain with RAG, reducing customer support ticket volume by 45% and improving response accuracy to 92%.
- Designed and deployed an intelligent document processing system using LlamaIndex, Pinecone, and FastAPI for a FinTech startup, enabling semantic search across 100K+ financial documents.
- Developed multi-agent AI workflows using CrewAI and LangGraph for a marketing agency, automating lead research, content generation, and outreach — saving 20+ hours/week of manual work.
- Created AI-powered automation pipelines using n8n, Make, and Zapier integrated with CRMs (GoHighLevel, HubSpot), reducing manual data entry by 80% for a real estate firm.
- Delivered prompt engineering and LLM fine-tuning consulting for multiple clients, optimizing AI model outputs for customer support, content creation, and data extraction use cases.
- Built production-ready REST APIs with Python and FastAPI to serve AI models on AWS and GCP, handling 10K+ daily requests with 99.9% uptime.
Hamza K.
Last position:
Academic Research Contributor in Health Sector (Volunteer)
- Acted as technical consultant to optimize multi-layer ensemble models combining ResNet, CNN-BiGRU-Attention, and XGBoost.
- Guided implementation of a Logistic Regression meta-learner to solve class imbalance problems, achieving 92.86% accuracy and 0.9644 AUC on PTB-XL and Chapman-Shaoxing datasets.
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)
Louis G.
Last position:
Freelance Solutions Architect and Machine Learning Engineer at Self-employed
- Develop and demonstrate solutions using GenAI software like langchain, vercel ai sdk, copilotkit
- Work with customers to understand their challenges and provide the best solutions based on open-source data products
- Build RAG and GraphRAG solutions using Neo4j, lancedb, and Postgres
- Deploy a LLMOps platform using kubernetes, terraform, helmfile, Arize phoenix, mlflow
- Architect and build data pipelines using dbt, Trino, Spark, Iceberg, Airflow, ArgoCD, terraform, kubernetes
- Delivered user-centred technical strategy for Agriculture 4.0 and precision livestock farming, helping my client secure funding from Bpifrance
- Delivered a prospecting tool for a leading French solar carport installer, using geospatial computing (GIS), speeding up the sales process
- Built digital twin architecture for solar carports and EV chargers, making real-time monitoring and smart charging possible
Jeet P.
Last position:
Global SAP Program Manager at Aldi Sued
- Pioneered first enterprise AI-SAP integration at ALDI SÜD, deploying AI-driven automation within one of retail's largest SAP S/4HANA programs, eliminating 50% of manual pre-cycle validation time and establishing replicable automation framework across 11 countries
- Led end-to-end SAP project lifecycle management for implementations across SAP S/4HANA and Manhattan Systems, supporting 7,300+ ALDI SÜD locations globally across Europe and Australia
- Served as primary executive liaison to C-level stakeholders across 11 countries for strategic SAP transformation programs
- Orchestrated automation, performance, and volume testing for critical releases, maintaining 99.9% system SLA compliance during peak retail periods
- Managed cross-functional international teams of 15+ specialists, delivering projects 20% faster than industry benchmarks
- Standardized SAP processes across 11 countries as part of one of retail's largest SAP implementations
- Directly managed €2M budget with 98% allocation accuracy across 12 concurrent projects
- Reduced SAP S/4HANA migration costs by 18% through strategic vendor contract renegotiations and optimization
Albert F.
Last position:
Lead Product Owner at CMBlu Energy AG
- Lead Product Owner for 4 development teams
- Leading and coordinating a greenfield project with parallel implementation of core components by independent teams; managing dependencies and resources
- Establishing a data lakehouse approach, including analysis of data volumes and future requirements as part of a cloud migration (best-of-breed approach)
- Responsible for requirements analysis, selection, and piloting of a LIMS/ELN system, supported by advising decision-makers and managing external vendors
- Introducing and managing an OpenWeb UI and Azure OpenAI-based RAG system to support knowledge extraction and data-driven analyses
- Setting up, configuring, and managing Jira projects, as well as developing project-specific workflows and automations
- Implementing classic Scrum processes with all ceremonies and taking on the Scrum Master role for all involved teams
- Assisting in hiring through interviews and assessments from a product owner's perspective
- Making key architectural decisions, including selecting the platform for the data lakehouse (Databricks) and the strategic integration of LIMS and analytics platforms
Lazaros K.
Last position:
RAG Webinar: Deep Dive and Use Cases at SHI GmbH
- Design, preparation and delivery of a webinar on 'RAG in Practice: How publishers create real value with AI'
- Preparing technical and strategic content on Retrieval Augmented Generation (RAG) for a mixed audience from the publishing industry
- Presenting specific use cases, technical backgrounds, common challenges and solution approaches when using RAG
- Providing practical insights into data preparation, model selection and output optimization in the context of digital publishing portals
- Conceptual and technical preparation of the webinar
- Selecting and presenting practical use cases from the publishing environment
- Developing technical backgrounds for implementing RAG systems
- Presenting and explaining typical challenges and solution strategies
- Large Language Models (LLMs)
- Retrieval Augmented Generation (RAG)
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
Tino T.
Last position:
Director Technology at Forte Digital Germany
- Leading 20+ staff in development, site reliability engineering, and architecture.
- Leading the group-wide agentic AI initiative (Norway, Poland, Germany).
- Hands-on solution architect and AI consultant for over 50% of my working time on client projects in the publishing sector – from local publishers to international corporations.
- Strategic consulting and technical implementation of AI workflow platforms (n8n, Workato).
- Developing prototypes for traditional, AI-based, and agentic AI workflows.
Kashyap K.
Last position:
Master’s Thesis - Synthetic Data Generation for Quality Inspection at Schaeffler Technologies AG
- Developed a synthetic data generation framework using 3D simulation (NVIDIA Omniverse) and Generative AI (Stable Diffusion) to model and augment industrial surface defects.
- Trained and evaluated Computer Vision models (YOLO, DETR), achieving 94% detection accuracy on real-world samples and demonstrating successful simulation-to-reality transfer.
- Applied domain adaptation to improve simulation-to-reality transfer, enabling scalable Industrial AI for automated quality inspection and reducing manufacturing downtime.
Discover over 15,000 top freelancers
Statistics of experts using LlamaIndex
Aggregated from the professional profiles of matched freelancers.
Experience
13 years
Position duration
2.3 years
Positions per freelancer
8
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Education, Professional Services
Certification focus areas
Information Technology, Product Development, Business Intelligence
Bachelor's degree or higher
100%
Master's degree or higher
85%
Doctorate
22%
Certifications per freelancer
2
Most common languages
English, German, Persian
Speak two or more languages
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 Germany 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 Germany 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 (93%)
- Education (45%)
- Professional Services (45%)
- Media and Entertainment (34%)
- Automotive (31%)
- Banking and Finance (31%)
- Manufacturing (28%)
- Retail (28%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What LlamaIndex does
LlamaIndex is a framework for connecting large language models with private and changing data. It helps teams build retrieval-augmented generation applications that answer questions from documents, databases, APIs and business systems. Its Python and TypeScript libraries support data ingestion, indexing, retrieval and response generation.
Core applications
LlamaIndex is used for knowledge assistants, research tools and internal search experiences. It can ground model responses in controlled sources instead of relying only on a model’s training data.
- Build question-answering systems over company documents
- Create chat interfaces for structured and unstructured data
- Add citations, metadata filters and source-aware responses
- Connect business systems to language model workflows
Ecosystem and tooling
Strong LlamaIndex professionals work across loaders, nodes, indices, retrievers, query engines and response synthesizers. They select vector stores such as Pinecone, Weaviate, Chroma or PostgreSQL with pgvector, and connect model providers including OpenAI, Anthropic and local models. Evaluation, observability and deployment tools are equally important.
When specialists help
Companies bring in freelance expertise when a prototype must become a dependable product, when data sources are fragmented, or when retrieval quality is difficult to diagnose. In Germany, this can support document-heavy industries, industrial knowledge bases and multilingual customer or employee services. Remote collaboration works well when data access, security requirements and language expectations are agreed early.
- Define chunking, metadata and indexing strategies
- Improve retrieval and reduce unsupported answers
- Integrate permissions and private enterprise data
- Prepare monitoring, evaluation and production rollout
Skills around LlamaIndex
The technology sits between data engineering, application development and machine learning operations. Professionals should understand embeddings, vector search, prompt design, structured outputs, API integration and data protection. They also need to manage latency, token use, failure handling and access control without weakening answer quality.
What strong professionals deliver
A capable specialist starts with the user’s questions and the source data, not with a preferred index type. They test retrieval and generated answers against representative cases, expose citations and design clear fallback behaviour. Strong delivery includes maintainable ingestion jobs, measurable evaluation criteria, secure deployment and documentation that lets the client operate the system after handover.
Frequently asked questions
The facts hiring teams ask for most often when it comes to LlamaIndex.
LlamaIndex is used to build applications that let language models work with private or frequently changing data. Common examples include document assistants, enterprise search, research workflows and natural-language interfaces for databases and APIs.
LlamaIndex focuses strongly on data ingestion, indexing and retrieval for knowledge applications, while LangChain offers a broader orchestration approach for chains, agents and tool use. They can also be used together, so the right choice depends on the application architecture and the team’s existing components.
A strong LlamaIndex specialist should understand embeddings, vector databases, prompt design, Python or TypeScript, API integration and evaluation. Experience with access control, observability and deployment is also important for enterprise systems.
The required expertise depends on the risk and scope of the application, not just on the framework. A proof of concept may need focused retrieval and data preparation skills, while a production system requires testing, security, monitoring, cost control and reliable update workflows.
LlamaIndex can process German documents and multilingual source collections when the selected models, loaders and retrieval settings support them well. A specialist should test terminology, metadata, citations and language consistency with representative company content before wider rollout.
Remote work is often practical for LlamaIndex projects because ingestion, retrieval and application design can be managed through shared repositories and controlled environments. On-site work may still help when specialists must access restricted systems, coordinate with business teams or review sensitive data handling.
Ask how the professional would measure retrieval quality, handle incomplete sources and prevent unsupported answers. A strong LlamaIndex freelancer can explain chunking, metadata, access permissions, evaluation sets, citations and production monitoring in terms of your actual data and user needs.
A typical LlamaIndex engagement should produce an ingestion design, retrieval pipeline, model and vector-store integration, evaluation approach and deployment documentation. Depending on scope, it may also include a working interface, source citations, monitoring dashboards and operational handover.
The average hourly rate of freelancers in Germany who have used LlamaIndex in their recent projects is 88 €, which corresponds to a daily rate of about 708 € based on an 8-hour working day.
Of the freelancers in Germany who have used LlamaIndex in their recent projects, 100% hold at least a Bachelor's degree, 85% hold at least a Master's degree, and 22% hold a doctorate.
On average, freelancers in Germany who have used LlamaIndex in their recent projects have 13 years of professional experience, with a single engagement typically lasting around 2.3 years.
The most common languages among freelancers in Germany who have used LlamaIndex in their recent projects are English (100%), German (90%), and Persian (10%).
The most common industries among freelancers in Germany who have used LlamaIndex in their recent projects are Information Technology (93%), Education (45%), and Professional Services (45%).
The most common business areas among freelancers in Germany who have used LlamaIndex in their recent projects are Information Technology (97%), Product Development (93%), and Research and Development (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 
Munich Request a free demo
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Philipp Thomaschewski
FRATCH CEO
