
LlamaIndex Experts in Berlin
, matched with vetted and available freelancers in minutesHire experts who connect language models to private data, design retrieval-augmented generation pipelines and integrate production-ready evaluation and observability. Get a precise match with vetted, available freelancers for your LlamaIndex project.
Meet FRATCH Experts in Berlin, who have recently used LlamaIndex
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.
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.
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
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.
Meisam G.
Last position:
Senior AI Engineer / Data Scientist at Geeks Ltd (WordUp)
Geeks Ltd is a UK-based technology company; WordUp is its AI-driven language-learning product focused on personalized vocabulary learning and intelligent educational experiences.
- Coordinate AI product delivery across Product, Engineering, Data, Operations, and leadership, translating user needs into scoped initiatives, sequencing work, surfacing blockers, facilitating hand-offs, and communicating progress.
- Own search, recommendation, retrieval, and content-enrichment features end to end, from requirements and architecture through Python/FastAPI implementation, testing, deployment, monitoring, and rapid iteration.
- Developed low-latency retrieval, ranking, and personalization services using AWS, OpenSearch, DynamoDB, embeddings, and reusable APIs, achieving <1s latency, 22% higher engagement, and 12% higher premium conversion.
- Use AI coding assistants for codebase analysis, scaffolding, refactoring, tests, debugging, and documentation while reviewing every output for correctness, architectural fit, security, maintainability, and user value.
- Represent technical work in planning and stakeholder discussions, gather requirements first-hand, challenge priorities constructively, explain delivery trade-offs, and help teammates make outcome-focused decisions.
Apoorv S.
Last position:
AI Interviewer
- Built an AI Research Assistant with RAG, LangChain, LangGraph, and OpenAI LLMs integrated with vector search.
Muskan V.
Last position:
AI Engineer at Sagas IT Analytics
- Built an AI Research Assistant with RAG, LangChain, LangGraph, and OpenAI LLMs integrated with vector search; cut research time by 30%.
- Designed custom retrieval workflows with LlamaIndex, building a ReAct-style agent for dynamic chunking; improved query accuracy by 18%.
- Researched and optimized embedding strategies, reducing retrieval cost/query by 15%.
- Developed RAG evaluation frameworks using RAGAS and Langsmith with custom datasets; improved coverage by 40%.
- Fine-tuned LLMs (LLaMA 2 on Vertex AI with custom inference containers, dynamic batching, and quantization); reduced inference latency by 25%.
- Integrated AI agents in LangGraph with short-term & long-term memory (Mem0); increased task completion rate by 20%.
- Created schema-aware synthetic data generators; fine-tuned downstream models achieving +12% F1 score.
Roman D.
Last position:
CTO at EFS
- Introduced a serverless/event-driven platform, boosting deployment frequency from 3 to 40 per month
- Implemented an LLM-based fraud-detection proof of concept that flagged 92 % of suspicious transactions
- Built performance and DORA metrics dashboards adopted by the C-suite
Salar M.
Last position:
Project management and tutor at LLMs Learning Journey
- Designed and managed an upskilling academy for Deutsche Telekom.
Discover over 15,000 top freelancers
Statistics of experts using LlamaIndex
Aggregated from the professional profiles of matched freelancers.
Experience
12 years (Germany: 13 years)

Position duration
2.5 years (Germany: 2.3 years)

Positions per freelancer
6 (Germany: 8)

Top business areas
Information Technology, Product Development, Research and Development

Top industries
Information Technology, Professional Services, Banking and Finance

Certification focus areas
Information Technology, Product Development, Project Management
Bachelor's degree or higher
100%
Master's degree or higher
80% (Germany: 85%)
Doctorate
10% (Germany: 22%)

Certifications per freelancer
2

Most common languages
English, German, Persian

Speak two or more languages
80% (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 Berlin 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 Berlin 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%)
- Professional Services (60%)
- Banking and Finance (40%)
- Media and Entertainment (40%)
- Education (30%)
- Healthcare (30%)
- Retail (30%)
- Automotive (20%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What LlamaIndex does
LlamaIndex is an open-source framework for building applications that let large language models work with private and changing data. It connects documents, databases, APIs and business systems to models through indexing, retrieval and structured workflows. Teams use it for question answering, research assistants, document analysis and agentic applications.
Core building blocks
The framework provides data connectors, readers, indexes, retrievers, query engines and response synthesizers. Specialists work with vector stores, metadata filters, chunking strategies and embedding models to make retrieval relevant and traceable. LlamaIndex can connect with model providers such as OpenAI, Anthropic and local models, alongside storage systems including PostgreSQL, Elasticsearch and dedicated vector databases.
Typical project work
- Ingest contracts, manuals, tickets and knowledge-base content
- Build retrieval-augmented generation pipelines with citations
- Create agents that call tools, APIs and internal services
- Add evaluation sets, tracing and retrieval quality checks
- Deploy secure LlamaIndex services behind existing applications
Projects often combine Python application work with data preparation, prompt design and API integration. In Berlin, specialists may support local product, research, finance or industrial teams through remote delivery, on-site workshops or a blended setup.
When expertise matters
Companies bring in freelance expertise when a prototype must become a dependable product, when answers need evidence from proprietary sources, or when several data systems must be unified. A specialist is also valuable when retrieval quality is inconsistent, indexing costs grow, permissions are unclear or an existing LangChain-based application needs a more structured data layer.
Skills beyond the framework
Strong professionals understand information retrieval, embeddings, vector search and model behaviour rather than treating LlamaIndex as a simple plug-in. They can design ingestion pipelines, handle document versioning, preserve access controls and expose useful observability. Experience with FastAPI, async Python, cloud deployment, containers and databases helps turn a working demo into a maintainable service.
Signs of quality
Look for a clear explanation of why a particular index, retriever and chunking method fit the data. Good specialists test groundedness, relevance, latency and failure handling with representative questions. They separate retrieval problems from model problems, protect sensitive content and document trade-offs so the system can be operated by the internal team after delivery.
Frequently asked questions
Everything clients usually want to know about LlamaIndex, in one place.
LlamaIndex is used to connect large language models with private or domain-specific data. It supports retrieval-augmented generation, document question answering, knowledge assistants, structured extraction and agents that use business tools.
LlamaIndex places particular emphasis on data ingestion, indexing and retrieval for language-model applications. LangChain offers broader orchestration patterns, so the right choice depends on whether the central challenge is knowledge access, workflow coordination or a combination of both.
A strong LlamaIndex specialist usually combines Python, embeddings, vector databases, information retrieval and prompt design. Useful adjacent skills include FastAPI, SQL, cloud deployment, access control, evaluation, tracing and integration with model providers or local models.
The required experience depends on the risk and scope of the system, not only on the framework. A prototype may need focused retrieval and data-connection skills, while a production service requires experience with evaluation, security, observability, deployment and ongoing data updates.
Yes. LlamaIndex projects are well suited to remote collaboration because code, data schemas and evaluation sets can be reviewed online. Berlin teams should agree on data access, documentation, meeting cadence and whether occasional on-site workshops or German-language communication are needed.
Ask the specialist to explain the ingestion flow, retrieval strategy and evaluation method using representative questions. High-quality LlamaIndex work provides source citations where appropriate, measures retrieval and answer behaviour, handles permissions and documents known failure cases.
LlamaIndex can work with local and self-hosted models as well as hosted model providers. The specialist must check model compatibility, embedding quality, hardware constraints, response latency and how sensitive data moves through each component.
Before starting, clarify the source systems, document formats, access rules, target users and definition of a useful answer. LlamaIndex work also benefits from early agreement on evaluation data, deployment ownership, observability and how frequently indexes must be refreshed.
The average hourly rate of freelancers in Berlin, Germany who have used LlamaIndex in their recent projects is 96 €, which corresponds to a daily rate of about 768 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used LlamaIndex in their recent projects, 100% hold at least a Bachelor's degree, 80% hold at least a Master's degree, and 10% hold a doctorate.
On average, freelancers in Berlin, Germany who have used LlamaIndex in their recent projects have 12 years of professional experience, with a single engagement typically lasting around 2.5 years.
The most common languages among freelancers in Berlin, Germany who have used LlamaIndex in their recent projects are English (100%), German (80%), and Persian (20%).
The most common industries among freelancers in Berlin, Germany who have used LlamaIndex in their recent projects are Information Technology (100%), Professional Services (60%), and Banking and Finance (40%).
The most common business areas among freelancers in Berlin, Germany who have used LlamaIndex in their recent projects are Information Technology (100%), Product Development (100%), and Research and Development (70%).
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.
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