Retrieval-Augmented Generation Experts in Switzerland
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Meet FRATCH Experts in Switzerland, who have recently used Retrieval-Augmented Generation
Gwang Jin Kim
Last position:
Data Scientist / Applied AI, Automation & Data Systems Researcher at Independent
- Built and explored applied GenAI, RAG, GraphRAG, local LLM, agentic AI and document-intelligence prototypes for structured analysis, evidence extraction, semantic search, technical reasoning and decision-useful reporting
- Developed private local-LLM workflows and AI system patterns focused on privacy, reproducibility, reviewability, low-cost inference and practical user control
- Built reproducible Python/R workflows for data analysis, automation, API-driven tooling, validation logic, technical documentation and AI-assisted software development
- Designed workflows around explicit assumptions, traceable inputs, reviewable outputs and failure-mode awareness rather than black-box “looks good” demonstrations
- Supported RAHN AG in a chemical/regulatory environment with data extraction and processing around WERCS, a regulatory application for chemical product and compliance data
- Explored complex application/database schemas and wrote nested SQL queries to extract information for mixture calculations, component relationships, regulatory rules and reporting logic
- Continued hands-on development in Git/GitHub/GitLab/Bitbucket, Docker/Linux deployment patterns, REST/API workflows, error handling, technical writing and fast AI-assisted prototyping
- Built technical writing and documentation workflows that turn complex systems into clear runbooks, checklists, decision notes and user-facing explanations
Robin Ochsner
Last position:
Co-Founder & AI Solutions Architect at airdys
- Product strategy, architecture, and technical co-direction
- AI workshops, client onboarding and go-to-market activities
- Design and implementation of AI architectures (LLMs, RAG, MCP, agents, voice, automation)
- Hands-on development of prototypes and production-ready AI integrations
- Consulting clients on AI adoption, workflows, and integration into existing infrastructure
- Collaboration with co-founders on strategic direction
- Collaboration in sales and customer acquisition
Tools and Technologies: OpenAI, Anthropic, Azure, Vercel AI SDK, RAG (Retrieval-Augmented Generation), MCP (Model Context Protocol), FastAgent, VAPI, n8n, make.com, LibreChat, PostgreSQL, OpenAPI, Next.js, Vercel, Docker
Ralf Ramge
Last position:
Security Architect at Federal Employment Agency
Responsibility for the design, migration, and integration of a security- and audit-critical HashiCorp Vault platform in the trust center of a nationwide authority with system-critical importance.
Analysis and realignment of the existing HashiCorp Vault landscape, including production and planned use cases
Design, proof-of-concept, migration, and integration of HashiCorp Vault Enterprise, taking into account the authority's PKI and operational processes
Design and implementation of an automated certificate and secrets management system for 300 Kubernetes clusters and several thousand certificates in the trust center
Securing the Vault platform using hardware security modules (HSM)
The solution enables a highly available, audit-compliant, and automated operation of certificate and secrets use cases in a highly regulated environment.
Technologies used: HashiCorp Vault Enterprise, Terraform, Ansible, OpenSSL, PKI, HSM
Daniel Schmidt
Last position:
Senior Manager at devpoint GmbH
- Design and co-implementation of an insolvency management platform in Germany for a client (based on an AI development tool)
- Concept, requirements engineering, and implementation support for a Dubai-based company to integrate processes into a CRM
- Business analysis and requirements engineering at Swisscom for integrations
- Integration of a new knowledge management system into the business processes at Swisscom AG – REST API definition
- Project manager, consultant, and sparring partner for the realignment/process digitalization at GIB Solutions AG
- Process designer and prototype for an AI-based real estate marketing system in Dubai
- Interim head of the ICT department at a telecom company, reorganizing and optimizing processes with a team of 5 at GIB Solutions AG
- Agile requirements engineer / external PO for a web-based solution for the German company DEHN AG
- Project management and consulting for the existing marketing and campaign planning solution at Swisscom AG
- Building the ALoHA nearshore offering at devpoint
Stefan Hess
Last position:
Fullstack Development, Product Owner & Tech Lead at Trex AG
- Business analysis, architecture, and implementation of a telemedicine platform for pet owners.
- Leading the development team as Product Owner and Tech Lead.
- Introducing agile processes, setting up development guidelines and system documentation.
- Planning and implementing features like video calls, live chat, and marketing automation.
- Implementing AI-based features such as automated tagging of information (missing pet reports, marketplace entries, etc.), preparation of social media content, and processing of conversation transcripts.
- Skills: Angular, NGXS, Tailwind, Java, Spring Boot, Kubernetes, Docker, CI/CD, MySQL, LLMs, RAG, MCP, Redis, OpenSearch.
- Industry: Veterinary medicine.
Fabian Kostadinov
Last position:
Lecturer at HWZ University of Applied Sciences
- Co-teach in CAS AI Management and CAS AI Innovation programs for future AI managers
- Cover topics including data platforms, AI architecture, technology adoption foundations, and factors influencing enterprise AI initiative success
Matthias Isler
Last position:
Fractional CTO (Principal Engineer / Technical Architect)
- Designed large-scale systems and APIs serving thousands of concurrent users.
- Refactored a 650k-LOC monolith and led full AWS migration for stable performance.
- Introduced SLO-based observability, improving reliability and recovery flow.
- Optimised cloud and databases, achieving significant cost and latency reduction.
- Delivered LLM, RAG, and document-automation pipelines adopted in production.
Oleksii Bondarenko
Last position:
Blockchain Developer (freelance) at Self-Employed
- Developed ERC20 smart contracts with a mechanism for vesting, including deployment, testing, and auditing.
- Wrote a JavaScript application for managing transactions in Bitcoin, Ethereum, and SEPOLIA with a custom token.
Discover over 15,000 top freelancers
Statistics of experts using Retrieval-Augmented Generation
Aggregated from the professional profiles of matched freelancers.
Experience
21 years
Position duration
2.5 years
Positions per freelancer
10
Top business areas
Information Technology, Product Development, Marketing
Top industries
Information Technology, Banking and Finance, Professional Services
Certification focus areas
Information Technology, Product Development, Project Management
Bachelor's degree or higher
100%
Master's degree or higher
71%
Doctorate
14%
Certifications per freelancer
3
Most common languages
English, German, French
Speak two or more languages
100%
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 Switzerland 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 Switzerland 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 it does
Retrieval-Augmented Generation, often called RAG, combines a language model with retrieval from your own content. It is used to answer questions from documents, knowledge bases, product data and support records without fine-tuning every fact into the model.
Where it fits
- Internal knowledge assistants and policy search
- Customer support copilots and help centers
- Semantic search over PDFs, tickets and wikis
- Research tools that cite source passages
Core stack
Strong specialists work with embeddings, chunking, reranking, vector databases and prompt design. They also understand APIs, data pipelines, document parsing and evaluation so the system returns grounded answers, not just fluent text.
Common work
Companies bring in freelance experts when they need a clean proof of concept, a production search flow or help after a first build stalls. They review retrieval logic, improve context selection, reduce hallucinations and shape the output format for real users.
What good work looks like
A strong RAG specialist tests retrieval with real queries, inspects missed passages and watches how the model uses context. They care about source quality, latency, fallbacks and traceability, not only the final answer.
Switzerland focus
In Switzerland, RAG is often used for multilingual knowledge access, regulated internal documentation and cross-team support. Remote work is common, but on-site sessions can help when teams need access to sensitive content, internal vocabularies or tight review loops.
Frequently asked questions
Not sure where to start with Retrieval-Augmented Generation? These answers cover the essentials.
Retrieval-Augmented Generation is used to let a language model answer from your own documents, records or knowledge bases instead of relying only on training data. Teams use it for support assistants, internal search, policy lookup, and document question answering. It is most useful when facts change often or must stay grounded in source material.
RAG adds retrieval at query time, while fine-tuning changes the model itself. Many teams choose RAG when the content changes frequently, needs citations, or comes from a large private corpus. Fine-tuning can still help with style or format, but it does not replace retrieval for current facts.
A strong retrieval-augmented generation specialist usually knows embeddings, chunking, reranking, vector search, prompt design and evaluation. Useful adjacent skills include document parsing, API integration, data cleanup and observability. If your content is messy, data modeling and search relevance experience matter a lot too.
A RAG project often needs outside help when the first prototype works on simple questions but fails on real user queries. Freelance experts are useful for retrieval tuning, source selection, prompt structure, and production hardening. They can also help if your team needs a short burst of focused delivery without long onboarding.
Retrieval-Augmented Generation is most often compared with plain LLM prompting, search-only systems, and fine-tuning. Plain prompting is simpler but weaker on private or changing knowledge. Search-only systems find documents well, but they do not turn them into fluent answers as effectively.
A good RAG build answers the right question, uses the right sources, and avoids making unsupported claims. Look for clear evaluation on real queries, stable context selection, and answers that point back to the source material when needed. Latency and failure handling also matter in production.
Yes, Retrieval-Augmented Generation projects often work well remotely, especially when the content is already in digital form. In Switzerland, some teams still prefer on-site sessions for discovery, governance, or access to sensitive material. A good freelancer should be comfortable with either setup.
For a small pilot, RAG can be handled by one specialist who knows search, LLM prompting and evaluation. For a production system, look for someone who has shipped retrieval pipelines, handled source quality issues, and worked with monitoring or feedback loops. The harder the content and compliance needs, the more valuable that depth becomes.
The average hourly rate of freelancers in Switzerland who have used Retrieval-Augmented Generation in their recent projects is 122 €, which corresponds to a daily rate of about 974 € based on an 8-hour working day.
Of the freelancers in Switzerland who have used Retrieval-Augmented Generation in their recent projects, 100% hold at least a Bachelor's degree, 71% hold at least a Master's degree, and 14% hold a doctorate.
On average, freelancers in Switzerland who have used Retrieval-Augmented Generation in their recent projects have 21 years of professional experience, with a single engagement typically lasting around 2.5 years.
The most common languages among freelancers in Switzerland who have used Retrieval-Augmented Generation in their recent projects are English (100%), German (88%), and French (50%).
The most common industries among freelancers in Switzerland who have used Retrieval-Augmented Generation in their recent projects are Information Technology (100%), Banking and Finance (75%), and Professional Services (75%).
The most common business areas among freelancers in Switzerland who have used Retrieval-Augmented Generation in their recent projects are Information Technology (100%), Product Development (88%), and Marketing (38%).
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:
Zurich Geneva Basel Bern Request a free demo
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Philipp Thomaschewski
FRATCH CEO
