Retrieval-Augmented Generation Experts in Austria
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Meet FRATCH Experts in Austria, who have recently used Retrieval-Augmented Generation
Alexander P.
Last position:
Owner & Lecturer at Own company for AI governance and data products, Vienna
- Consulting and interim management at the interface between IT operations and regulation
- Impact analysis and implementation planning for NISG 2026 and the EU AI Act, including risk management and reporting and evidence processes
- Training for governing bodies and employees on regulatory obligations
- Lectures in Data & Information Management and Human-Machine Interaction at University of Applied Sciences Burgenland, since 2023
- Supervision of master’s theses and participation in the examination board
- Presentations for business and educational institutions
- Design and development of data and AI products, platforms and pipelines
- Privacy-first architectures and zero-knowledge encryption, cloud-native on EU infrastructure
- MLOps and AIOps in live operations
- Own applications under own brand: shared codebase, separate delivery for each target device
- AI-assisted software development (vibe coding), complete agentic pipelines, code generation, implementation, automated testing, CI/CD and release cycles
- Publications on the EU AI Act, NIS2, DORA, CRA and CER as an integrated governance system
- Publications on data sovereignty, cloud economics and industrial image processing
- AI governance / compliance: data quality, Responsible AI, EU AI Act readiness, risk classification, AI ethics
Manuel P.
Last position:
AI Engineer at Misumi Europe GmbH & Motius GmbH
- Designed and built a next-generation NLP platform to accelerate sales-driven customer service through intelligent request analysis and routing, reducing average customer query response time by 30%.
- Architected a hybrid NLP system combining Large Language Models (LLMs) with traditional NLP pipelines for robust, explainable results.
- Developed request classification and routing mechanisms to accelerate customer support teams in handling customer queries faster and more accurately.
- Optimized LLM based data extraction and classification with context engineering.
- Integrated the platform into customer service processes, reducing response times and enhancing workforce efficiency.
Daniel S.
Last position:
AI Automation in E-Commerce at Looops
- AI automation roadmap for a D2C/B2B e-commerce company.
- Customer service bot with RAG over support tickets and product data, OCR pipeline for incoming invoices with writeback to Business Central, lead gen and posting automation.
- Deterministic n8n workflows with EU-hosted models.
- n8n, RAG / Mistral, Qwen/BGE embeddings / Business Central API, HubSpot, Shopify / Scaleway, S3 / Claude Code, OpenCode.
Roman M.
Last position:
Software Engineer at FSM Rechtsanwälte
- Develop AWS-based components connecting Python backend services to React/TypeScript frontends.
- Improve database queries and application workflows for document processing and screening.
- Evaluate LLM retrieval and document-review workflows as part of the product stack.
Marcel S.
Last position:
Senior AI Engineer - Python at Insurance Company
Project Tech Stack: Python, AWS, Azure, FastAPI, openai, pandas, unittest/pymock
Achievements:
- Engineered automated data extraction pipelines to transform complex Excel datasets into structured formats via LLM-driven workflows.
- Architected a generative slide-deck engine that translates natural language prompts into formatted presentation assets.
- Integrated advanced LLM capabilities with the OpenAI Response API, implementing sophisticated tool-calling and structured output logic.
- Developed and containerized scalable backend microservice using FastAPI, Docker, and OpenShift to host and serve agentic skills.
Wolfgang F.
Last position:
AI Driving License Trainer at KI-Trainer WIFI Wien
Slavi S.
Last position:
Head of AI Enablement at REEVO Tech
- Architected enterprise AI strategy delivering 25% cost reduction through multi-LLM platform integration (Claude, OpenAI, Gemini) with intelligent routing and fallback optimization
- Drove operational excellence achieving 85% reduction in manual documentation and 3x faster campaign production through AI workflow automation and agent deployment across Slack and Microsoft Teams
- Established AI governance framework from ground up, implementing EU AI Act compliance, data redaction protocols, and safety filters aligned with DPIA standards
- Scaled AI adoption through structured onboarding programs, developing departmental workflows with secure access controls and comprehensive audit trails
- Implemented advanced RAG systems using Elasticsearch, Vertex AI, and Azure AI Foundry to improve knowledge access and output reliability
- Managed delivery through OKR framework coordinating cross-functional squads across 6-week project cycles to ensure strategic alignment and measurable results
Martin H.
Last position:
Software Development & IT-Consulting at Self-Employed
Tech stack: Flutter/Dart, Unity3D, React, NextJS, JavaScript, TypeScript, HTML, CSS, SASS, SCSS, Tailwind, Bootstrap, Ollama, llama.cpp, LangChain, MCP, RAG, n8n, Node-Red, PlatformIO, Arduino, ESP, C, C++, Python/Django, NodeJS, C#, .NET, PostgreSQL, MySQL, MS-SQL, MongoDB, AWS, Azure, GCP, GitHub Actions, GitLab CI, Azure Pipelines, Ansible, Terraform, Docker, Postman, REST, WebSocket, Figma, Penpot, Adobe Illustrator, Adobe InDesign, VS Code, Android Studio, Visual Studio, Unity3D, Jira, Confluence, Traefik, Rclone, Authentik, pfSense, Cloudflare, Proxmox, MQTT
Previous projects:
n8n automation that reads incoming emails from an Office mailbox and routes them to stakeholders
FastMCP 2 server exposing internal services via REST API to an LLM
Terraform migration of cloud infrastructure with Azure VMs, network, blob, storage and cloud-init
Azure Pipelines for automated testing, deployment, and artifact management
Terraform, Ansible, Cloudflare Tunnel integration with Docker container provisioning and backups
GitHub Actions CI/CD for Flutter app builds, testing, automated screenshots, and App Store deployment
Flutter app with SSO, local database, backend integration, BLE synchronization
GCP setup with Cloud DNS, App Engine, Cloud SQL
AWS environment with EC2, S3, Route 53, ECS
Workshops delivered on Docker, Git, Terraform, Ansible, Cloudflare, DevOps, modern software development practices
Backend development with Python Django REST Framework, PostgreSQL, MQTT
SSO identity provider with OIDC, OAuth, LDAP, Cloudflare Tunnels, Authentik
Christian M.
Last position:
Senior Feature Engineer - Banking at start.me app e.U.
- Requirements engineering & business analysis
- Architecture responsibility for core banking extensions
- Interface modeling for third-party systems (TiGital, ESA, ÖNB)
- AML, KYC & payment reporting
Discover over 15,000 top freelancers
Statistics of experts using Retrieval-Augmented Generation
Aggregated from the professional profiles of matched freelancers.
Experience
18 years
Position duration
1.8 years
Positions per freelancer
15
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Banking and Finance, Education
Certification focus areas
Information Technology, Project Management, Research and Development
Bachelor's degree or higher
100%
Master's degree or higher
86%
Certifications per freelancer
3
Most common languages
German, English, Spanish
Speak two or more languages
100%
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 Austria 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 Austria 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 19 Sep 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 (100%)
- Banking and Finance (67%)
- Education (56%)
- Healthcare (56%)
- Professional Services (56%)
- Retail (56%)
- Food and Beverage (44%)
- Transportation (44%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What RAG does
Retrieval-Augmented Generation, often called RAG, links a language model to external knowledge before it answers. Instead of relying only on model memory, it retrieves relevant passages from documents, wikis, tickets, or databases and uses them to ground the response. That makes it useful for support assistants, internal search, and knowledge-heavy products.
Core components
A strong setup usually combines search, embeddings, reranking, and prompt design.
- document ingestion and chunking
- vector database or hybrid search
- retrieval quality checks and reranking
- prompt templates with citations
- evaluation for grounded answers
Where it fits
Companies bring in RAG specialists when they need accurate answers over private content. Common use cases include policy assistants, product documentation search, sales enablement tools, and case-handling copilots. In Austria, this often matters for firms that work in regulated, multilingual, or document-heavy environments where plain chat is not enough.
What strong specialists know
Good professionals understand both information retrieval and LLM behavior. They know how to reduce hallucinations, keep context windows under control, and choose between chunking strategies, metadata filters, and hybrid retrieval. They also think about source freshness, access control, and how users will review or trace an answer.
Typical project signs
- answers are vague or unsupported
- internal documents are hard to search
- the model misses the right source
- citations or traceability are required
- content changes often and must stay current
Delivery and teamwork
RAG work often starts with a proof of concept and then moves into production hardening. Teams may need help with data pipelines, evaluation sets, latency, and fallback behavior when retrieval finds nothing useful. In Austria, remote collaboration is common, but on-site sessions can help when teams need access reviews, domain workshops, or input from local subject matter experts.
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 must answer from company content instead of guessing from general training. It fits internal search, support assistants, policy Q&A, product help, and knowledge tools that need current or private information. The main goal is grounded answers with a clear source trail.
RAG pulls relevant information at query time, while fine-tuning changes model behavior through training. A plain chatbot can sound fluent but may miss company-specific facts, and fine-tuning alone does not solve freshness or source lookup. RAG is usually the better fit when the content changes often or must be cited.
A strong Retrieval-Augmented Generation specialist usually knows embeddings, vector search, prompt design, and evaluation. Experience with Python, APIs, document pipelines, and access control also helps a lot. For larger systems, search ranking, observability, and basic data engineering matter too.
Retrieval-Augmented Generation expertise matters when the answer must be traceable, the source set is large, or retrieval quality affects user trust. It also matters if the system needs hybrid search, multilingual content, or strict document permissions. In those cases, small design mistakes quickly become visible in production.
RAG projects are often well suited to remote work because most tasks sit in code, search configuration, and evaluation. On-site sessions can still help at the start, especially for workshops with domain experts, security reviews, or access to sensitive content. Many Austrian teams use a mixed setup.
Around Retrieval-Augmented Generation, specialists often work with vector databases, full-text search, document stores, and LLM APIs. They may also use rerankers, OCR for scanned files, and evaluation tools for answer quality. The exact stack depends on whether the focus is chat, search, or internal knowledge access.
Look for someone who talks about retrieval quality, not just prompt writing. A strong Retrieval-Augmented Generation freelancer can explain chunking choices, citation behavior, failure cases, and how they test grounded answers. Good signs are concrete examples, measurable evaluation methods, and clear thinking about security and freshness.
Freelancers working on RAG should expect messy source data, changing requirements, and close collaboration with domain experts. The work is often part search, part application design, and part evaluation. The best projects leave room to improve retrieval, answer quality, and user feedback over time.
The average hourly rate of freelancers in Austria who have used Retrieval-Augmented Generation in their recent projects is 102 €, which corresponds to a daily rate of about 818 € based on an 8-hour working day.
Of the freelancers in Austria who have used Retrieval-Augmented Generation in their recent projects, 100% hold at least a Bachelor's degree and 86% hold at least a Master's degree.
On average, freelancers in Austria who have used Retrieval-Augmented Generation in their recent projects have 18 years of professional experience, with a single engagement typically lasting around 1.8 years.
The most common languages among freelancers in Austria who have used Retrieval-Augmented Generation in their recent projects are German (100%), English (100%), and Spanish (44%).
The most common industries among freelancers in Austria who have used Retrieval-Augmented Generation in their recent projects are Information Technology (100%), Banking and Finance (67%), and Education (56%).
The most common business areas among freelancers in Austria who have used Retrieval-Augmented Generation in their recent projects are Information Technology (100%), Product Development (100%), and Business Intelligence (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.
Countries:
- Germany
- Austria
- Switzerland
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Philipp Thomaschewski
FRATCH CEO
