LlamaIndex Experts in Berlin
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Meet FRATCH Experts in Berlin, who have recently used LlamaIndex
Hamza K.
Last position:
Academic Research Contributor in Health Sector (Volunteer) at n/a
- 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.
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.
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
Position duration
2.7 years
Positions per freelancer
6
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
90%
Doctorate
10%
Certifications per freelancer
2
Most common languages
English, German, Persian
Speak two or more languages
80%
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 Berlin 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 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 29 Oct 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 (50%)
- Banking and Finance (40%)
- Media and Entertainment (40%)
- Education (30%)
- Healthcare (30%)
- Retail (30%)
- Energy (20%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Context-Augmented LLM Systems
LlamaIndex, formerly known as GPT Index, serves as the data framework for orchestrating context-augmented large language model applications. The platform provides data ingestion connectors, structured index structures, and advanced retrieval engines. Organizations leverage it to ground foundation models on private internal documentation and real-time operational databases.
Core Engineering Deliverables
- Hierarchical document parsing and automated metadata extraction
- Hybrid search combining dense vector embeddings and BM25 sparse retrieval
- Multi-document agent orchestration with query planning
- Fine-tuned reranking setups with reciprocal rank fusion
- Automated evaluation suites measuring context relevance and groundedness
Connected Ecosystem and Tooling
Engineering workflows around the framework depend on tightly integrated infrastructure. Solutions pair with vector databases such as Qdrant, Milvus, and pgvector for semantic search. Data ingestion leverages integrations with cloud object storage, internal wikis, and enterprise issue trackers, while observability layers trace token costs, latency spikes, and retrieval degradation across multi-step query pipelines.
Practical Implementations in Berlin
Berlin companies apply the framework across fintech, healthtech, and enterprise software. Local tech teams build automated compliance review pipelines, domain-specific internal search engines, and multi-tenant customer support agents. Freelance specialists frequently join Berlin startups and established scaleups to accelerate production deployments and refactor fragile experimental prototypes.
Triggers for Engaging External Specialists
Companies hire external professionals when baseline vector search produces hallucinations or stale context. Production demands call for advanced indexing patterns like recursive retrieval, sentence window parsing, and knowledge graphs. Bringing in focused expertise resolves performance bottlenecks, reduces token overhead, and introduces systematic continuous evaluation before customer release.
Hallmarks of Strong Domain Specialists
Top practitioners demonstrate deep familiarity with Python, modern retrieval architectures, and chunking strategies. They design modular ingest pipelines that handle high-throughput updates without corrupting index consistency. Strong professionals know how to benchmark response quality quantitatively and select the correct index abstractions for nuanced corporate data landscapes.
Frequently asked questions
Everything clients usually want to know about LlamaIndex, in one place.
LlamaIndex is primarily used to build retrieval-augmented generation systems that ingest, structure, and query private enterprise knowledge. It transforms raw text, structured tables, and APIs into queryable indexes for foundation models. Organizations deploy it to power autonomous research agents, document analytics platforms, and conversational search tools.
While LangChain operates as a general-purpose orchestration library for LLM chains and tools, LlamaIndex specializes specifically in data ingestion, indexing, and retrieval mechanics. Many engineering teams combine both tools, using the framework for deep retrieval tasks and feeding the resulting context into broader multi-agent ecosystems.
A capable LlamaIndex professional should have strong command of Python, vector databases like Qdrant or Pinecone, and embedding model selection. Experience with data loaders, reranking algorithms, and evaluation frameworks such as Ragas or TruLens is essential for maintaining production-grade accuracy.
Yes, most LlamaIndex projects run smoothly in fully remote or hybrid arrangements across the Berlin time zone. When handling sensitive corporate data or on-premise infrastructure, local specialists can also join on-site sessions for technical discovery and architectural alignment.
Teams usually adopt GPT Index and its modern iterations when simple text similarity queries yield inaccurate, generic, or hallucinated LLM responses. It is necessary whenever systems must query complex PDFs, maintain relational context across multiple documents, or run multi-stage reasoning over diverse internal data sources.
Specialists test LlamaIndex setups by running automated evaluation suites on context precision, context recall, and faithfulness. They track retrieval latency, experiment with hybrid search and cross-encoders, and continuously refine metadata tagging to eliminate false positives in production environments.
Berlin is an international tech hub where English is the standard working language for AI development teams. When LlamaIndex workflows parse local administrative documents or German compliance records, specialists with bilingual English and German proficiency ensure parsing rules and extraction schemas function correctly.
A targeted retrieval prototype using LlamaIndex can be established within a few weeks by an experienced professional. Moving to enterprise-grade production with automated ingestion, document access controls, and evaluation pipelines generally requires an engagement across several weeks to a few months.
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, 90% 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 2.7 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 (50%), 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.
Countries:
Berlin 
Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
