Retrieval-Augmented Generation Experts in Dortmund
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Meet FRATCH Experts in Dortmund, who have recently used Retrieval-Augmented Generation
Christian F.
Last position:
Project Lead & Cyber Security Consultant at Deutsche Börse Gruppe
- Established a cyber resilience framework for AI-accelerated threats in the Frontier AI Threat Endurance (FATE) program: capability assessments, maturity assessments and gap analyses covering cyber security, operational resilience, incident response, recovery and governance, followed by target concepts and improvement measures
- Supported action owners from specification through implementation: implementation roadmaps, decision papers, management presentations and governance reporting, as well as audit- and supervision-ready documentation; managed stakeholders from IT, Information Security, Risk Management, Operational Resilience, Business Continuity and the business units
Ashwin P.
Last position:
Freelance Data Scientist at Mercor Intelligence
- Architected and deployed end-to-end machine learning pipelines across classification and prediction datasets, ensuring robustness and reproducibility through MLOps best practices.
- Contributed directly to LLM model output accuracy improvement by designing and engineering specialised prompts grounded in end-to-end ML and SciML pipeline logic.
- Developed training data for large language models by formulating coding problems that models could not resolve and subsequently documenting the correct solutions.
Nemanja M.
Last position:
AI Engineer / Senior Backend Engineer at Intelycx
Manufacturing intelligence platform with enterprise workflows, RAG, real-time AI assistant features, and multi-repository backend architecture.
- Built and extended production AI/backend services with Django, DRF, FastAPI, GraphQL, Celery, PostgreSQL, MySQL, Redis, and WebSockets across a modular multi-repository platform.
- Contributed to ARIS V2, a real-time manufacturing AI assistant using LangChain, LangGraph, MCP tool orchestration, planning/execution flows, OpenAI, AWS Bedrock, Qdrant, and Elasticsearch/OpenSearch-backed retrieval.
- Supported rollout expansion from ARIS V1 in 4 of 17 client production plants to ARIS V2 currently active in 13 of 17 plants, increasing real-world deployment coverage to more than 50% of the client footprint.
- Worked on document-grounded RAG functionality including ingestion, OCR, chunking, embeddings, indexing, retrieval, reranking, and grounded answer generation for industrial workflows.
Stack: Python, Django, DRF, FastAPI, LangChain, LangGraph, GraphQL, Celery, WebSockets, OpenAI, AWS Bedrock, Qdrant, Elasticsearch/OpenSearch, PostgreSQL, MySQL, Redis, Docker.
Laurin H.
Last position:
Software Architect (Freelance) at Care4Sure
- Delivered MVP-focused full-stack architecture for a health-sector client: Vite/React frontend, backend services on Google Cloud Run, and Supabase for database plus IAM/authentication.
- Supported product requirements engineering and prioritized cost-aware workload placement, implementing browser-side/edge computation where feasible before moving logic to backend services.
Kersten L.
Last position:
Lead Architect / Lead Developer at Bettles: Sports Betting Platform
- Complete greenfield rebuild across the whole stack — built AI-native: backend in Go and NestJS, PostgreSQL (CNPG) on K3s with GitOps/Terraform; frontend on Angular 22, zoneless.
- Orchestrated coding agents (e.g. Claude Code, Cursor) across the entire lifecycle — architecture, implementation, testing, reviews, documentation — driven by Specification-Driven Development (SDD).
- “Bruno” — LLM commentator persona backed by RAG and MCP for a personality that stays consistent across all generations (match previews, post-match reports, his own virtual bets).
Angular 22 (zoneless, without Zone.js), Claude Code, Claude Code Skills, CNPG, Cursor, Design Tokens (Spec for Code), Docker, Gherkin, Git, GitLab, GitOps, Go, Google Gemini, Grafana, Hetzner Cloud, K3s, Keycloak, Kubernetes, Lighthouse, LLM Integration, Model Context Protocol (MCP), NestJS, Node.js, NPM, Playwright, PostgreSQL, Prometheus, RAG, REST, Specification-Driven Development (SDD), Structured Outputs, Terraform, TypeScript, Vitest
Mojtaba P.
Last position:
AI Voice / RAG / Automation Engineer & Technical Project Manager at Technolohit
- Technical design, prioritization, and hands-on implementation of AI automation, conversational AI, voice AI, and private AI solutions.
- Translation of business requirements into system architecture, conversation/workflow logic, APIs, data flows, and production-ready AI workflows.
- Development of backend and integration components with Python/FastAPI, PostgreSQL/pgvector, Redis, REST APIs, webhooks, and background jobs.
- Design and implementation of RAG architectures with local/private LLMs, embeddings, document processing, semantic search, and controlled access to company knowledge.
- Prompt engineering for LLM and voice/conversational AI applications; iterative optimization based on real processes, system responses, and quality requirements.
- Practical work with ElevenLabs and its APIs in a voice AI project, as well as technical evaluation and integration of voice AI components.
- Deployment and operation of Docker-based systems on Linux with Nginx, CI/CD, monitoring/logging, backup/restore, and reproducible deployment processes.
Patrik G.
Last position:
Technical Lead Conversational AI at CANCOM
- Technical lead of a team developing agentic chatbot solutions (React, TypeScript, Python, FastAPI)
- Architecture design for multi-LLM dialog systems - focus on maintainability, UX, and autonomous execution
- Stakeholder alignment, CI/CD processes, and AI integration at enterprise level
Christian W.
Last position:
Interim Business Analyst / Product Owner at Bundesdruckerei GmbH (via FourEnergy GmbH)
- Initial assessment of requirements based on a business value prioritization framework
- Identification of issues as well as requirement gathering and evaluation using UML, BPMN, and design thinking methods for iterative requirements analysis through interviews and workshops
- Use of user story mapping in Miro to visualize and align functional requirements (e.g. correct transmission of all application data and attachments to the specialist system) as well as non-functional requirements (e.g. complete and verifiable deletion of an applicant's data) with stakeholders
- Proactive stakeholder management of internal and external stakeholders from public authorities, business units, organizations, and companies
- Preparation of status reports to communicate project progress and upcoming tasks transparently
- Responsibility for a REST-based integration solution (middleware) for secure data exchange between core systems and external specialist applications; ensuring stability and performance in day-to-day operations
- Support for Product Owners in prioritizing backlog items and in product discovery
- Communication of planning to internal and external stakeholders as well as interim assumption of Product Owner tasks and responsibilities during a staff change
Mohammed A.
Last position:
Data Scientist & Energy Consultant at Accenture GmbH
Discover over 15,000 top freelancers
Statistics of experts using Retrieval-Augmented Generation
Aggregated from the professional profiles of matched freelancers.
Experience
16 years (Germany: 15 years)
Position duration
1.8 years (Germany: 2.8 years)
Positions per freelancer
10
Top business areas
Information Technology, Product Development, Project Management
Top industries
Information Technology, Healthcare, Education
Certification focus areas
Information Technology, Product Development, Project Management
Bachelor's degree or higher
100% (Germany: 97%)
Master's degree or higher
43% (Germany: 75%)
Doctorate
14% (Germany: 12%)
Certifications per freelancer
3
Most common languages
German, English, French
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 Dortmund 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 Dortmund 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 (89%)
- Healthcare (67%)
- Education (56%)
- Professional Services (56%)
- Automotive (44%)
- Energy (44%)
- Government and Administration (44%)
- Retail (44%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Core principle
Retrieval-Augmented Generation, commonly called RAG, combines information retrieval with language model generation. Before producing an answer, the system searches selected sources and adds relevant passages to the model context. This helps assistants answer from current, private or domain-specific material instead of relying only on training data.
What it builds
RAG supports internal knowledge assistants, customer service tools, research interfaces and document question-answering. It can ground responses in manuals, contracts, product records, support tickets or technical documentation. The best implementations return useful citations, handle missing information clearly and keep sensitive data within approved boundaries.
- Search company documents with natural language
- Generate grounded answers with source references
- Connect assistants to structured and unstructured data
- Refresh answers when source content changes
Ecosystem and tooling
A RAG system typically combines an embedding model, a vector database and an orchestration layer. Common choices include OpenAI or open-source language models, Elasticsearch, OpenSearch, Pinecone, Weaviate, Milvus, pgvector, LangChain and LlamaIndex. Strong specialists also work with document parsers, rerankers, metadata filters, evaluation frameworks and observability tools.
When expertise matters
Companies bring in freelance specialists when a prototype must become a dependable product, internal data is difficult to search or answers need clear evidence. Expertise is also valuable when retrieval quality is inconsistent, context windows are costly or access rules must be enforced across departments. In Dortmund, remote delivery can support local industrial, logistics, research and service organisations while allowing workshops on site when useful.
Delivery concerns
Successful projects start with source selection, chunking strategy, metadata design and measurable answer criteria. Specialists map permissions through ingestion and retrieval, protect personal and confidential data, and test failure cases such as conflicting documents or unsupported questions. They also connect pipelines to existing APIs, cloud services, identity systems and monitoring.
- Define the knowledge scope and user journeys
- Prepare, clean and index source material
- Tune retrieval, prompts and reranking
- Evaluate grounding, relevance and refusal behaviour
- Operate the pipeline with versioned data and alerts
Strong professionals
A strong RAG professional understands information retrieval as well as language models. They can explain why an answer was produced, separate retrieval errors from generation errors and choose simple search when it is more reliable than a complex chain. Look for clear evaluation methods, careful security decisions, maintainable integrations and experience translating business knowledge into dependable search experiences.
Frequently asked questions
Before you brief your next project: the most common questions about Retrieval-Augmented Generation.
Retrieval-Augmented Generation is used to create assistants and search experiences that answer from approved external content. Typical applications include internal knowledge bases, support automation, document analysis, technical research and product guidance.
RAG retrieves current information at query time, while fine-tuning changes a model’s learned behaviour through additional training. RAG is often easier to update and audit for changing company content; fine-tuning can be useful for response style, task patterns or specialised behaviour, and the approaches can be combined.
A strong Retrieval-Augmented Generation specialist usually understands embeddings, vector search, reranking, prompt design and language model APIs. Data preparation, access control, evaluation, Python or TypeScript, cloud deployment and observability are also valuable for production work.
The right level for RAG depends on the scope, data quality and risk of the application. A focused prototype may need one specialist who can design retrieval and test answers, while a production system may require deeper expertise in security, evaluation, data pipelines and operations.
Retrieval-Augmented Generation work is well suited to remote collaboration because source repositories, cloud environments and evaluation tools can be shared securely. Teams in Dortmund may still prefer on-site workshops for domain discovery, data access decisions or user testing, with language expectations agreed at the start.
RAG can retrieve from PDFs, office files, websites, wikis, ticket systems, databases and application APIs. A specialist should assess extraction quality, document structure, update frequency and permission rules before selecting an ingestion and indexing approach.
A capable Retrieval-Augmented Generation professional defines test questions based on real user needs and checks retrieval relevance, answer accuracy, citation quality and appropriate refusal. They should also show how the system handles incomplete, outdated or conflicting sources rather than presenting confident guesses.
Retrieval-Augmented Generation projects often involve strict reviews of data access, hosting, documentation and stakeholder approval, especially when company or personal information is involved. Freelancers working with organisations in Dortmund should be ready for structured collaboration, clear security requirements and communication in the language the project team uses.
The average hourly rate of freelancers in Dortmund, Germany who have used Retrieval-Augmented Generation in their recent projects is 91 €, which corresponds to a daily rate of about 730 € based on an 8-hour working day.
Of the freelancers in Dortmund, Germany who have used Retrieval-Augmented Generation in their recent projects, 100% hold at least a Bachelor's degree, 43% hold at least a Master's degree, and 14% hold a doctorate.
On average, freelancers in Dortmund, Germany who have used Retrieval-Augmented Generation in their recent projects have 16 years of professional experience, with a single engagement typically lasting around 1.8 years.
The most common languages among freelancers in Dortmund, Germany who have used Retrieval-Augmented Generation in their recent projects are German (100%), English (100%), and French (22%).
The most common industries among freelancers in Dortmund, Germany who have used Retrieval-Augmented Generation in their recent projects are Information Technology (89%), Healthcare (67%), and Education (56%).
The most common business areas among freelancers in Dortmund, Germany who have used Retrieval-Augmented Generation in their recent projects are Information Technology (89%), Product Development (89%), and Project Management (67%).
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 
Frankfurt 
Stuttgart 
Dusseldorf 
Essen 
Nuremberg Request a free demo
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FRATCH CEO
