Retrieval-Augmented Generation Experts in Frankfurt
, matched in minutes with vetted, available specialistsHire experts who connect language models to trusted company data, design retrieval pipelines and evaluate grounded answers. FRATCH matches you quickly and precisely with vetted, available freelancers for remote or on-site work in Frankfurt.
Meet FRATCH Experts in Frankfurt, who have recently used Retrieval-Augmented Generation
Patrick L.
Last position:
Senior GenAI Fullstack Developer at SBH (Schulbau Hamburg)
Remote freelance role focused on Agentic AI strategy, secure application patterns, and reusable agentic workflows for a government agency.
- Development and implementation of an open-source Agentic AI strategy for a government agency, with a focus on GDPR, security, and self-hosted solutions
- Development of reusable agentic workflows and business applications that enable non-technical employees to solve business problems independently
- Implementation of nine business applications with Single Sign-On (SSO) and Azure PostgreSQL integration on Hetzner Linux servers
Techstack: Python, Streamlit, Anthropic SDK (Claude), Azure, Linux, PostgreSQL, MS SQL, Angular
Saqib J.
Last position:
AI Developer / AI Engineer (Lead) at KOM4TEC GmbH
- Designed and implemented modular AI assistants for sales and business processes within the Microsoft ecosystem (Agentic AI, Copilot extensions)
- Frontend architecture and development with React + TypeScript for embedded chat and assistant surfaces (streaming UI, Hooks, React Query, OpenAPI clients)
- Enterprise-level agent development: reusable skill/agent library, MCP servers, review and compliance gates
- LLM integration into the user experience: Anthropic (Claude), OpenAI, tool use, RAG pipelines, prompt engineering, guardrails
- Architecture and code review consulting, and mentoring within the AI development team
- Integration with Microsoft Graph, Power Platform, and Azure services
- Technologies: React, TypeScript, Anthropic Claude, OpenAI, MCP, RAG, Microsoft Graph, Power Platform, Azure
Ali A.
Last position:
Founder & Architect at Independent AI R&D
- Fully on-premises LLM document-examination platform for a compliance-critical banking domain: agentic LangGraph pipeline with deterministic verification, every AI judgment structured and source-anchored; ~960 automated tests, zero data egress
- GPU throughput engineering (quantized serving, speculative decoding, prefix caching): 9.5x extraction speed-up, 500+ multi-document case files per day on a single A100
- AI-native EDI/EDIFACT integration platform (~116k LOC Java 25 / Spring Boot 4, 1,900+ tests): LLM-drafted partner mappings machine-verified before go-live (DFDL conformance, field-coverage checks, dry runs), ~99.5% byte match on real customer files — replacing weeks of manual mapping per partner
Anthony M.
Last position:
Research and Development, AI for Enterprise at Mwanachama
- Built an MCP (Model Context Protocol) layer for Mwanachama's agency service, turning domain manager methods into callable AI-agent tools. This included a composite tool that builds a full organization design (org chart, goals, workflows, RACI matrix) from one specification.
- Built the chat-driven agency builder (Wakala Studio and API), where an organization describes its structure in natural language and an AI agent uses those tools to construct and modify the live design.
- Added an insights service so an organization can review AI-agent interactions and completed work. Insights from that review feed back into solution design, gated by architect and user sign-off.
- Alongside this, designed and built the platform itself: ~20 Go microservices on PostgreSQL, Flutter and React clients, deployed on Kubernetes.
- AI agents scan the platform autonomously for security gaps and run scripted tests, covering API (Postman-style) and UI testing. The rest of the work stays supervised. No rogue agents, promise.
- Building an AI-native enterprise platform that structures organizational data, workflows and responsibilities for both people and AI agents.
- Models accountability through RACI, allowing human and AI participants to take different operational roles, including Responsible, Accountable, Consulted and Informed. AI agents can also take controlled approval roles where the organization assigns that responsibility.
- Extended the organizational and operational-risk themes from my Executive MBA thesis into software, making roles, processes and controls explicit and machine-readable.
- Structures enterprise context so LLM interactions use more relevant information instead of repeatedly processing large amounts of unstructured content.
Minh D.
Last position:
Project Manager / Business Analyst / Application Manager at Finance and Insurance
Introduction of 5 different process applications for different teams
Release planning: scope and time management
Resource/capacity planning
Conducting Sprint Planning / retrospectives
Increment planning (several sprints)
Preparation of Steering Committee meetings / reporting to the Board
Coordination / alignment with external suppliers / deliveries
Resource planning for multiple projects
Coordination with business department and development team
Identification of best practices with IBM BAW
Cost control and planning for the project team and external service providers
Collection of KPIs using LogScale
Analysis of application errors with LogScale / Queries
User Story definitions / coordination of requirements with the business department and development team
Testing and defect tracking
UI/UX design of applications
Preparation and moderation of Brown-Paper workshops
Test concept, test data, test organization, test execution
Collection of team velocity / metrics
Test execution
Script for automated testing
Test organization with business department and IT
Recording and prioritization of defects
Establishment and operation of the application
Establishment of application monitoring with LogScale Dashboards
Checking health endpoints with PowerShell
Post Mortem analysis
Establishment of Incident Management
Establishment of Problem Management
Analysis of errors using LogScale Queries and Dashboard
Pre-processing of data for AI
Evaluation using AI language models (Meta Llama 3.3 LLM and deepset Haystack) and RAG
Installation of runtime environments for LLMs (Large Language Model)
Evaluation of various LLMs
Installation of RAG (Retrieval Augmented Generation) and integration with LLM
Extraction of unstructured data with LLM and RAG
Project based on IBM BAW (Business Automation Workflow), Websphere Liberty, Domea, d.3, REST, LogScale (formerly Humio), Swagger, Powershell, JIRA, Confluence, Lucom Interaction Platform (LIP), Mattermost, Jabber
Eduard V.
Last position:
Lead of workshop “Introduction to AI Development Tools” at Software company in Wiesbaden
- Presentation introducing generic A.I. and Large Language Models
- Explanation of legal frameworks (EU AI Act, US CLOUD Act, DSGVO)
- Systematic review of AI tools along the SDLC and holistic systems
- Comparison of on-prem LLMs vs. cloud-based solutions, as well as change management and works council considerations
- Moderation of the discussion and definition of next steps for introducing AI development tools
Mathew D.
Last position:
Data Science Expert and AI Strategist at Freelancer
- Built an API to ingest, clean, translate, and index EU tenders documents in Neo4j, enabling hybrid search with RAG and Cypher queries via a Streamlit dashboard
- Deployed the API on AWS Lightsail container services with CI/CD automation via GitHub Actions, ensuring stability through pytest unit and integration tests
- Designed and developed a comprehensive online course on data analysis using ChatGPT for professionals and learners, creating instructional videos and interactive Jupyter notebooks
- Utilized OBS and professional audio equipment to ensure high-quality video and audio content
- Led a CRM data normalization and cleaning project visualized via a Sankey diagram to aid customer understanding and pipeline development
- Implemented and validated a genAI-driven web crawling strategy on AWS, ensuring data quality, scalability, and CRM data augmentation
Noel L.
Last position:
Founder & Lead Engineer at ausbildung-in-der-it.de
- Platform established and running stably; deliberately reducing my involvement to refocus on an engineering mandate in the financial sector.
- Built an own SaaS learning platform from the ground up and scaled it to over 20,000 users (over 6,000 courses sold, B2C and B2B); end-to-end ownership from development through infrastructure to operations.
- Built a lab environment that provisions an isolated Linux container per user (Docker, Traefik, Go), including automatic provisioning and a dedicated subdomain per user.
- Integrated LLM features into the product and accelerated development end-to-end with AI-assisted workflows (Claude Code, Codex); CI/CD with automated tests.
Alona L.
Last position:
AI Architect
AI-powered platform for automated UX validation and designer support
- Designed and led technical implementation of an enterprise-wide AI solution for automated UX review that improved design quality and significantly reduced manual review processes in teams
- Developed an automated UX validation tool as a Figma plugin and web application that generates test cases based on internal guidelines and reliably checks current designs for consistency and standard compliance
- Implemented an interactive designer chat based on RAG that answers questions about the current design and the company's UX guidelines, and designed the deployment architecture using containerized services
- Python, Azure OpenAI, PostgreSQL, REST API, Docker, OpenShift, Helm, CI/CD, Figma MCP, LLM, RAG, Prompt Engineering, GenAI, XAI, AI Architecture, AI Strategy
Anton R.
Last position:
AI-Engineer at Publicly traded company, industrial safety technology
- Designed and implemented the agent-based AI architecture for a company-wide platform to securely deploy LLM-based agents
- Designed and implemented end-to-end RAG pipelines from multiple sources: document preprocessing, chunking strategies for different document types, embeddings, retrieval with re-ranking, and robust prompt orchestration
- Developed a modular context engineering framework with skill architecture, context isolation, and dynamic resource management; human-in-the-loop control for enterprise tool integrations
- Built the CI/CD pipeline, testing strategy, tracing on the software side as well as automated LLM and agent evaluations, red team testing and tracing, and handed over to a reproducible production environment (ISO27001 and SOC2 compliant)
Kevin M.
Last position:
Backend & Infrastructure Engineer at Mileo Systems GmbH
- Engineered production-ready Azure environments using Terraform, ensuring consistent infrastructure parity across VNets and Resource Groups
- Implemented Microsoft Fabric tenant and workspace architecture for multi-stage Medallion data processing pipelines
- Designed secure data pathways using Managed Private Endpoints for isolated Azure Storage access
- Managed Service Principals and authentication tokens for secure REST API integrations
Kabir K.
Last position:
AI Engineer / Banking IT Specialist at Hamburg Commercial Bank (HCOB) & Real Estate Firm
- Developed a retrieval-augmented generation (RAG) application using LangChain and LangGraph for corporate document parsing, delivered as an installable Electron desktop application with local AI models via Ollama.
- Currently providing ongoing AI feature support for the Loan Pricing Tool at Hamburg Commercial Bank, with a commitment of three days per month.
- Architected Kubernetes-native solutions, including Helm chart configuration and Azure DevOps pipeline integration.
Roman K.
Last position:
Senior Data Engineer / Cloud Architect at DB Systel
- Development of a central billing app for cloud costs at DB
- AWS
- Python
- AWS CDK
- RDS
- Spark (PySpark)
- Glue
- Lambda
- CI/CD (GitLab)
- React/Typescript
- data optimization
- Scrum
Jens D.
Last position:
Product Owner & Senior Data Scientist at Legal Tech
- Led an international team of six developers in a Scrum environment
- Defined strategic goals for the project in coordination with stakeholders and the development team
- Prompt engineering for language models to improve the accuracy and relevance of generated responses
- Implemented LangChain components for a RAG chatbot to answer legal questions
- Technologies: GPT-4, LangChain, Python (Pandas, sklearn, streamlit), Docker, GitLab, ChromaDB
Alf Z.
Last position:
Professor of Business Administration at Hamm-Lippstadt University of Applied Sciences
Discover over 15,000 top freelancers
Statistics of experts using Retrieval-Augmented Generation
Aggregated from the professional profiles of matched freelancers.
Experience
17 years (Germany: 15 years)
Position duration
2.2 years (Germany: 2.8 years)
Positions per freelancer
11 (Germany: 10)
Top business areas
Product Development, Information Technology, Business Intelligence
Top industries
Information Technology, Banking and Finance, Education
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
100% (Germany: 97%)
Master's degree or higher
45% (Germany: 75%)
Doctorate
18% (Germany: 12%)
Certifications per freelancer
3
Most common languages
German, English, Italian
Speak two or more languages
100% (Germany: 97%)
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 Frankfurt 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 Frankfurt using Retrieval-Augmented Generation
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.
Retrieval-Augmented Generation 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 (87%)
- Banking and Finance (67%)
- Education (47%)
- Manufacturing (40%)
- Telecommunication (33%)
- Automotive (27%)
- Healthcare (27%)
- Media and Entertainment (27%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What it does
Retrieval-Augmented Generation, commonly called RAG, combines information retrieval with text generation. A system searches approved sources for relevant passages, adds that context to a language model prompt, and produces an answer grounded in those sources. This helps companies build assistants that use current, private and domain-specific knowledge without retraining a model for every content change.
Where it fits
RAG supports internal knowledge assistants, customer service tools and research workflows. It can search product documentation, contracts, support records, policies or technical content before generating a response. In Frankfurt, specialists often support financial, industrial, logistics and professional services teams that need controlled access to business information.
- Answer questions over internal documents
- Create citation-aware research assistants
- Support multilingual knowledge search
- Ground customer and employee self-service
Ecosystem and tooling
Strong delivery connects document ingestion, parsing, chunking, metadata and access control with embeddings and vector search. Common choices include OpenAI or open-source language models, embedding models, rerankers, LangChain, LlamaIndex, Elasticsearch, OpenSearch, Pinecone, Weaviate, Milvus and PostgreSQL with pgvector. Quality also depends on APIs, observability, prompt design and evaluation datasets.
When expertise matters
Companies bring in freelance specialists when a proof of concept must become a dependable product, when search results are weak, or when sensitive data needs strict separation. They can assess source quality, select retrieval strategies, reduce unsupported answers and connect RAG to existing identity, content and workflow systems. Remote delivery works well, while on-site sessions in Frankfurt can help with stakeholder interviews and data mapping.
- Search results lack relevance or useful citations
- Model answers contain unsupported claims
- Document permissions are not respected
- Usage, latency and answer quality are hard to measure
What strong specialists deliver
The best professionals treat retrieval as a product capability, not only a prompt exercise. They define answer policies, test difficult queries, compare retrieval and reranking approaches, and make failures visible. They also explain trade-offs between hosted and open-source models, keep data flows traceable, and design fallback behaviour when no reliable source is found.
Skills beyond RAG
Useful adjacent skills include Python or TypeScript, API design, cloud deployment, data engineering and search relevance. Specialists should understand vector and keyword retrieval, hybrid search, document lifecycle management and model security. They should be comfortable working with legal, compliance, product and subject-matter teams, including German and English content where the project requires it.
Frequently asked questions
Need clarity? These are the questions we hear most often about Retrieval-Augmented Generation.
Retrieval-Augmented Generation is used to create applications that answer questions using a company’s own documents or live knowledge sources. Typical examples include internal search assistants, customer support tools, policy assistants and research workflows. The retrieved passages give the language model relevant context before it generates a response.
RAG supplies changing information at query time, while fine-tuning changes how a model responds based on training examples. RAG is usually more suitable for private or frequently updated knowledge because source documents can be revised without retraining the model. Fine-tuning may still help with tone, format or specialised behaviour.
A strong Retrieval-Augmented Generation specialist often combines Python or TypeScript with search, data pipelines, APIs and cloud deployment. Useful knowledge includes embeddings, vector databases, hybrid retrieval, reranking, access control, prompt design and evaluation. Experience with observability and model security is also important for production systems.
The right level depends on the scope and risk of the application. A simple document assistant may need focused retrieval and integration skills, while a production system requires expertise in permissions, evaluation, monitoring, failure handling and data governance. For regulated or customer-facing use cases, choose a specialist who can show how they tested grounding and source attribution.
Yes. Retrieval-Augmented Generation work is often suitable for remote collaboration because data modelling, pipeline design, implementation and evaluation can be handled online. Frankfurt-based teams may still prefer on-site workshops for stakeholder interviews, document mapping or security reviews, especially when several business units own the source content.
Ask how the specialist would measure retrieval relevance, answer grounding and unsupported claims. A capable RAG professional should discuss source parsing, chunking, metadata, permissions, citations, evaluation queries and fallback responses rather than focusing only on prompts or model selection. They should also explain how the design will handle outdated or conflicting documents.
Retrieval-Augmented Generation can work with PDFs, office files, web content, ticket systems, knowledge bases, databases and structured records. The source connector is only part of the task: content must be parsed, cleaned, indexed and filtered according to user permissions. Specialists should clarify how updates, deletions and duplicate information are handled.
Judge RAG quality with representative questions and expected source passages, not with a few impressive demonstrations. Check whether retrieval finds the right evidence, answers stay within that evidence, citations are useful, and the system declines questions it cannot support. Also review latency, access control, monitoring and behaviour when documents are incomplete or contradictory.
The average hourly rate of freelancers in Frankfurt, Germany who have used Retrieval-Augmented Generation in their recent projects is 98 €, which corresponds to a daily rate of about 782 € based on an 8-hour working day.
Of the freelancers in Frankfurt, Germany who have used Retrieval-Augmented Generation in their recent projects, 100% hold at least a Bachelor's degree, 45% hold at least a Master's degree, and 18% hold a doctorate.
On average, freelancers in Frankfurt, Germany who have used Retrieval-Augmented Generation in their recent projects have 17 years of professional experience, with a single engagement typically lasting around 2.2 years.
The most common languages among freelancers in Frankfurt, Germany who have used Retrieval-Augmented Generation in their recent projects are German (100%), English (100%), and Italian (13%).
The most common industries among freelancers in Frankfurt, Germany who have used Retrieval-Augmented Generation in their recent projects are Information Technology (87%), Banking and Finance (67%), and Education (47%).
The most common business areas among freelancers in Frankfurt, Germany who have used Retrieval-Augmented Generation in their recent projects are Product Development (100%), Information Technology (93%), and Business Intelligence (73%).
Main locations of FRATCH Experts, who have recently used Retrieval-Augmented Generation
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 
Stuttgart 
Dusseldorf 
Dortmund 
Essen 
Nuremberg Request a free demo
Get in touch with the FRATCH team and we will get back to you within 4 hours.
Would you rather directly get in touch?
We always have the time for a call or email!
Philipp Thomaschewski
FRATCH CEO
