Retrieval-Augmented Generation Experts in Frankfurt
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Meet FRATCH Experts in Frankfurt, who have recently used Retrieval-Augmented Generation
Ali Aminian
Last position:
Platform Engineer & Software Architect at Yatta GmbH
- Architected the Yatta Integration Layer – a config-driven integration platform on Java 25, Spring Boot 4 (WebFlux), Temporal, gRPC and Kafka, enabling new third-party integrations (e.g. AVS fulfillment) via declarative JSON configs with zero code changes.
- Designed and implemented Tink integration with 0Auth IBAN verification to enhance fraud prevention and account validation workflows with Adyen payByBank.
- Architected and implemented an OpenFGA-based authorization model for centralized management of users, groups, and fine-grained access control in the vendor portal.
- Architected and led delivery of the Yatta API Gateway platform using GraphQL Federation, providing a unified enterprise API layer across distributed microservices with centralized authentication, authorization and request orchestration.
- Replaced NGINX + NLB with Istio service mesh and AWS ALB; rolled out WAF, OAuth (Cognito), IP whitelisting and RBAC across environments.
- Migrated CDC from Confluent Cloud connectors to a self-hosted Kafka Connect + Debezium stack, reducing operational cost by ~80% across multiple environments.
- Implemented the Transactional Outbox pattern with Debezium for reliable, exactly-once event publishing to Kafka with Avro and Schema Registry.
- Migrated dunning/payment-recovery workflows from Airflow to Temporal, achieving 99.9% reliability for settlement handling.
- Optimised Apache Airflow with deferrable sensors to handle 1000+ concurrent DAG runs without scaling the worker pool.
- Refactored a monolithic Terraform codebase into 3 modular projects, cutting deployment time by ~45%.
- Stood up full observability with OpenTelemetry, Tempo, Prometheus and Loki; automated dev/staging/prod with ArgoCD, Image Updater and Helm.
- Collaborated with product, operations and engineering stakeholders to define scalable platform architecture and integration standards aligned with long-term business and operational goals.
Saqib Javed
Last position:
AI Developer / AI Engineer (Lead) at KOM4TEC GmbH
- Conceptual design and implementation of modular AI assistants for sales and business processes in 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 server, 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 as well as mentoring in 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
Minh Doan
Last position:
Project Manager / Business Analyst / Application Manager at Finance and Insurance
Introducing 5 different process applications for various teams
Release planning: scope and time management
Resource/capacity planning
Conducting sprint planning / retrospectives
Increment planning (multiple sprints)
Preparing steering committee meetings / reporting to the executive board
Coordinating / aligning with external suppliers / deliveries
Multi-project resource planning
Aligning with the business unit and development team
Identifying best practices with IBM BAW
Cost control and planning for the project team and external service providers
Collecting KPIs using LogScale
Analyzing application errors with LogScale / queries
Defining user stories / aligning requirements with the business unit and development team
Testing and defect tracking
UI/UX design of the application
Preparing and facilitating brown-paper workshop
Test concept, test data, test organization, test execution
Recording team velocity / metrics
Executing tests
Scripts for automated testing
Organizing tests with the business unit and IT
Recording and prioritizing defects
Setting up and operating the application
Setting up application monitoring with LogScale dashboards
Checking health endpoints with PowerShell
Post mortem analysis
Setting up incident management
Setting up problem management
Analyzing errors using LogScale queries and dashboard
Pre-processing data for AI
Conducting evaluation with AI language models (Meta Llama 3.3 LLM and deepset Haystack) and RAG
Installing runtime environments for LLMs (large language model)
Evaluating various LLMs
Installing RAG (retrieval augmented generation) and integrating with LLM
Extracting 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
Alona Liuzniak
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
Kevin Meinon
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 Khaleque
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.
Eduard Van Kleef
Last position:
Workshop Leader 'Introduction to AI Development Tools' at Software company in Wiesbaden
- Presentation introducing generic AI and large language models
- Explanation of legal frameworks (EU AI Act, US CLOUD Act, GDPR)
- Systematic review of AI tools along the SDLC and holistic systems
- Comparison of on-prem LLMs vs. cloud-based, as well as change management and works council
- Facilitated the discussion and derived next steps for introducing AI development tools
Mathew Divine
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
Roman Krivtsov
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 Daube
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
Anton Rösler
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)
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: 14 years)
Position duration
2 years (Germany: 2.8 years)
Positions per freelancer
12 (Germany: 9)
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Banking and Finance, Manufacturing
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
100% (Germany: 96%)
Master's degree or higher
43% (Germany: 76%)
Doctorate
14% (Germany: 13%)
Certifications per freelancer
4 (Germany: 3)
Most common languages
German, English, Italian
Speak two or more languages
100% (Germany: 96%)
Based on our profile pool as of 30 Aug 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Frankfurt 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 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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
What RAG does
Retrieval-Augmented Generation combines search with text generation. It lets a model pull relevant context from your own documents, product data, or knowledge base before it answers. That makes outputs more grounded and easier to trace.
Typical projects
- Internal knowledge assistants for support, sales, or operations
- Customer chat tools that answer from approved content
- Document search over policies, contracts, manuals, and tickets
- Domain-specific Q&A for regulated or technical teams
Key building blocks
Strong specialists work with embeddings, chunking, reranking, vector databases, and prompt design. They also connect RAG flows to APIs, identity layers, and content sources such as SharePoint, Confluence, databases, or file stores. In Frankfurt, this often matters for firms that need German and English content handled with care.
When to bring in specialists
Teams call in freelance experts when answers feel vague, retrieval misses the right source, or latency gets too high. They are also useful when a proof of concept must become a production service with logging, evaluation, and access control. The goal is stable behavior, not just a demo.
What good experts deliver
Good Retrieval-Augmented Generation specialists do more than wire up a model. They test retrieval quality, reduce hallucinations, design fallback paths, and create clear prompt and index strategies. They also document how content is ingested, updated, and monitored so the system stays usable.
Choosing the right fit
Look for people who have shipped RAG systems, not just experimented with them. Ask how they handle source freshness, evaluation sets, citation quality, and permission-aware retrieval. For Frankfurt projects, remote work is common, but on-site workshops can help when teams must align on data sources, language, and compliance.
Frequently asked questions
Need clarity? These are the questions we hear most often about Retrieval-Augmented Generation.
Retrieval-Augmented Generation is used when a model needs to answer from trusted company content instead of relying only on pre-trained knowledge. It is common for support assistants, policy search, internal knowledge tools, and document Q&A. The best results come when the system can retrieve the right source before it writes the answer.
A RAG system retrieves relevant documents at answer time, while fine-tuning changes the model itself and a plain chatbot has no built-in access to your content. That makes RAG easier to update when policies, products, or manuals change. It is often the better fit when traceability and fresh information matter.
A strong Retrieval-Augmented Generation specialist usually knows embeddings, chunking, reranking, prompt design, vector databases, and API integration. Experience with search relevance, access control, and evaluation is also valuable. If the project uses enterprise content, they should understand document structure and content hygiene.
A Retrieval-Augmented Generation project usually needs document ingestion, metadata design, access rules, evaluation, and monitoring. You also need a plan for source freshness and a way to see which passages supported each answer. Without those pieces, the system may look good in a demo but fail in daily use.
For a production RAG system, look for someone who has already delivered end-to-end work, not just notebooks or prototypes. They should be able to explain retrieval trade-offs, quality testing, and failure handling. If the use case is sensitive, ask for evidence of work with secure or regulated content.
Yes, most Retrieval-Augmented Generation work can be done remotely because the core tasks are system design, integration, and testing. In Frankfurt, remote collaboration is often enough once access to source systems and stakeholders is in place. On-site workshops help when teams need to map content owners, review language use, or align on data boundaries.
With Retrieval-Augmented Generation, weak retrieval often shows up as correct-sounding but unsupported answers, missed source documents, or repeated references to the wrong content. If the system struggles with query wording, permissions, or newer documents, the retrieval layer needs work. A good specialist will test these cases directly.
Ask for concrete examples of how the RAG system was measured and improved. Good work includes clear evaluation cases, source citations, low-friction content updates, and a retrieval strategy that matches the business need. You should also expect clean documentation so your team can maintain the system later.
The average hourly rate of freelancers in Frankfurt, Germany who have used Retrieval-Augmented Generation in their recent projects is 106 €, which corresponds to a daily rate of about 851 € 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, 43% hold at least a Master's degree, and 14% 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 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 (18%).
The most common industries among freelancers in Frankfurt, Germany who have used Retrieval-Augmented Generation in their recent projects are Information Technology (82%), Banking and Finance (64%), and Manufacturing (45%).
The most common business areas among freelancers in Frankfurt, Germany who have used Retrieval-Augmented Generation in their recent projects are Information Technology (100%), Product Development (100%), 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 
Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
