Retrieval-Augmented Generation Experts in Vienna
, matched with vetted and available freelancers in minutesHire experts who connect language models to trusted business data, design semantic search with vector databases, and deliver grounded question-answering systems. FRATCH finds the right vetted, available freelancer through fast, precise AI matching.
Meet FRATCH Experts in Vienna, who have recently used Retrieval-Augmented Generation
Alexander P.
Last position:
Owner & Lecturer at Artellico / Own Company for AI Governance and Data Products
- Consulting and interim management at the intersection of IT operations and regulation
- Impact analysis and implementation planning for NISG 2026 and EU AI Act, including risk management and reporting and evidence processes
- Training for governing bodies and employees on regulatory obligations
- Teaching assignments in Data & Information Management and Human-Machine Interaction at Hochschule Burgenland, since 2023
- Supervision of master’s theses and participation in the examination board
- Expert presentations in 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
- Connection to bank account systems via SEPA direct debit mandates and message formats according to ISO 20022 (pain.008, camt.053)
- MLOps and AIOps in ongoing operations
- Own applications under own brand: shared code base, 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 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
Matthias K.
Last position:
Requirements Engineer & Business Analyst at Media Company
Analysis of existing work, information and decision-making processes.
Identification and prioritization of suitable use cases for AI agents.
Elicitation of functional and non-functional requirements.
Design of collaboration between users and AI agents, including control and escalation mechanisms.
Support for the implementation of the AI agents and adapted keyboard hardware.
Business target vision for the AI-first transformation.
Structured and prioritized requirements for implementation.
Defined roles, interactions and control points between people and AI agents.
Preparation for the gradual automation of productive work processes.
Techstack: AI agents, LLMs, agent orchestration, RAG, APIs, automation, hardware
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.
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
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, Ă–NB)
- 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
11
Top business areas
Information Technology, Product Development, Project Management
Top industries
Information Technology, Banking and Finance, Healthcare
Certification focus areas
Information Technology, Product Development, Project Management
Bachelor's degree or higher
100%
Master's degree or higher
75%
Certifications per freelancer
3
Most common languages
German, English, Spanish
Speak two or more languages
100%
Based on our profile pool as of 16 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Vienna 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 Vienna 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 16 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 (88%)
- Banking and Finance (63%)
- Healthcare (63%)
- Professional Services (63%)
- Retail (63%)
- Education (50%)
- Transportation (50%)
- Manufacturing (50%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What RAG does
Retrieval-Augmented Generation, commonly called RAG, connects a language model to an external knowledge base before it creates an answer. The system retrieves relevant passages from documents, databases or APIs and places them in the model’s context. This helps companies build assistants that answer from current, controlled information instead of relying only on training data.
What companies build
RAG supports internal knowledge assistants, customer service tools, research interfaces and document question-answering applications. It can work with policies, manuals, contracts, product data and support records while preserving access controls and source references.
- Enterprise search with natural-language queries
- Grounded chat over private documents
- Retrieval pipelines for support and research
- Answers with citations and traceable sources
Core ecosystem
Strong specialists understand the full retrieval pipeline, not only the language model. Their toolkit may include embedding models, vector databases such as Pinecone, Weaviate, Milvus or pgvector, orchestration frameworks such as LangChain or LlamaIndex, and model APIs from providers such as OpenAI, Anthropic or open-source communities. They also work with Python, APIs, document stores and cloud infrastructure.
When to bring in expertise
Freelance expertise is useful when a proof of concept must become a secure production service, or when search quality remains inconsistent. Companies often need help selecting embedding models, splitting and enriching documents, tuning retrieval, connecting permission systems and measuring answer quality. In Vienna, projects may also involve multilingual content, regulated industries and collaboration across local and remote teams.
- Existing search fails to find relevant business context
- Model answers contain unsupported or outdated claims
- Documents need automated ingestion and refresh
- Access rules must carry through to generated answers
What strong professionals deliver
The best professionals start with the user’s information need and define how relevance will be judged. They design ingestion, chunking, metadata, hybrid search and reranking as one system. They test difficult queries, inspect retrieved context, protect sensitive data and make failures visible through evaluation sets, tracing and clear source citations.
Working with RAG specialists
A project benefits from a clear data inventory, representative questions and decisions about the required answer sources. Specialists can work remotely with teams in Vienna when repositories, security requirements and review processes are well documented; on-site workshops can help with stakeholder discovery and domain terminology. Look for evidence of production retrieval systems, thoughtful evaluation and practical experience integrating language models with existing software.
Frequently asked questions
Before you brief your next project: the most common questions about Retrieval-Augmented Generation.
Retrieval-Augmented Generation is used to give language models access to private, current or domain-specific information at answer time. Common applications include enterprise search, document assistants, support tools, research systems and question answering with source citations.
RAG retrieves relevant information from an external source, while fine-tuning changes how a model behaves through additional training. RAG is often better when content changes frequently or must remain traceable; fine-tuning can help with consistent style, format or task behavior.
A strong Retrieval-Augmented Generation specialist should understand embeddings, vector and hybrid search, document processing, prompt design and evaluation. Experience with APIs, Python, cloud services, data security and access-control integration is also valuable.
A small proof of concept may suit a professional with focused experience in retrieval pipelines and language-model APIs. Production work needs broader delivery experience, including data ingestion, permission handling, observability, evaluation and operational maintenance.
Yes. RAG projects often work well remotely when data access, security rules, review ownership and technical documentation are clear. Vienna-based teams may still prefer on-site workshops for domain discovery, especially when multilingual or regulated information is involved.
Share representative documents, typical user questions, target systems and the sources answers must cite. It also helps to define privacy constraints, permission rules, languages and how relevance and factual grounding will be assessed.
Ask how the Retrieval-Augmented Generation system evaluates retrieval relevance, answer accuracy, citation quality and refusal behavior. A capable professional can show test queries, inspect retrieved passages and explain how failures are monitored and improved.
Retrieval-Augmented Generation may be unsuitable when the required knowledge cannot be reliably indexed or when the task needs deterministic database operations rather than generated text. A structured search, rules engine, direct API integration or fine-tuned model may be a better fit.
The average hourly rate of freelancers in Vienna, Austria who have used Retrieval-Augmented Generation in their recent projects is 98 €, which corresponds to a daily rate of about 785 € based on an 8-hour working day.
Of the freelancers in Vienna, Austria who have used Retrieval-Augmented Generation in their recent projects, 100% hold at least a Bachelor's degree and 75% hold at least a Master's degree.
On average, freelancers in Vienna, 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 Vienna, Austria who have used Retrieval-Augmented Generation in their recent projects are German (100%), English (100%), and Spanish (38%).
The most common industries among freelancers in Vienna, Austria who have used Retrieval-Augmented Generation in their recent projects are Information Technology (88%), Banking and Finance (63%), and Healthcare (63%).
The most common business areas among freelancers in Vienna, Austria who have used Retrieval-Augmented Generation in their recent projects are Information Technology (100%), Product Development (100%), and Project Management (75%).
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:
Vienna 
Graz 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
