Retrieval-Augmented Generation Experts in Switzerland
from over 15,000 CVs with fast, precise AI matchingHire experts who connect language models to trusted company knowledge, design reliable retrieval pipelines and deliver production-ready RAG applications. FRATCH matches you quickly with vetted, available freelancers whose skills fit your project.
Meet FRATCH Experts in Switzerland, who have recently used Retrieval-Augmented Generation
Gwang Jin K.
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
Mohamad K.
Last position:
Senior Backend Developer at Standing on Giants
- Led architecture and end-to-end engineering delivery for community-driven SaaS platforms serving 2M+ monthly active users.
- Architected and led the migration of a monolithic Python/FastAPI and PostgreSQL database and LangChain with codebase to an event-driven microservices architecture on AWS EKS, sustaining 10x traffic growth from ~150 RPS to 1,500+ RPS with zero re-architecture cycles.
- Defined and enforced engineering standards across services including API contracts, observability baselines, and deployment topology, reducing production incidents by 55% and MTTR from 2 hours to under 25 minutes within 9 months.
- Redesigned the caching and query layer using multi-tier Redis caching and database indexing/partitioning, cutting p95 API latency from 850ms to 180ms (78% reduction) and database CPU load by 45%.
- Built CI/CD platform on GitHub Actions, Terraform, and Kubernetes (EKS) with blue-green and canary rollouts, increasing deployment frequency from ~2/month to 8-12/day and reducing lead time from 10 days to under 6 hours.
- Implemented contract testing, automated load testing, and observability SLOs using Prometheus, Grafana, and OpenTelemetry, raising platform availability from 99.5% to 99.95% (10x reduction in error budget burn).
- Led and grew a cross-functional team of 8 engineers across backend, frontend, and DevOps, scaling headcount from 4 to 8 with 85% retention; owned hiring, onboarding, performance reviews, and career development.
- Partnered with Product, Design, and Client Success leadership as primary technical decision-maker; translated business goals into technical roadmaps and drove build-vs-buy decisions on authentication, search, and AI tooling.
- Introduced AI-assisted development workflows including automated code review and a RAG-based internal knowledge assistant using Graph (GraphRAG, Neo4J), increasing sprint throughput by 30% across two quarters.
- Owned incident command and production support rotation; established runbooks, postmortem culture, and on-call SLOs, reducing weekend paging incidents by 70%.
- Developed and optimized Algorithms using python libraries like Numpy and Pandas.
Robin O.
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 R.
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 S.
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 H.
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 K.
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 I.
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 B.
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.
Ned O.
Last position:
Tech Lead at R3leaf GmbH
- Autonomously drafted architecture and delivered production code at startup pace
- Wrangled diverse geospatial formats (NETCDF, GeoTIFF, GML) into unified standards and built scalable climate data visualisations from hundreds of GBs of geodata in a production web app
- Mentored developers, facilitated AI skill sharing, and contributed to competitive strategy with C-Level leadership
Discover over 15,000 top freelancers
Statistics of experts using Retrieval-Augmented Generation
Aggregated from the professional profiles of matched freelancers.
Experience
19 years
Position duration
2.4 years
Positions per freelancer
10
Top business areas
Information Technology, Product Development, Operations
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
67%
Doctorate
11%
Certifications per freelancer
3
Most common languages
English, German, French
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 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 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 (70%)
- Professional Services (60%)
- Education (40%)
- Automotive (30%)
- Healthcare (30%)
- Insurance (30%)
- Media and Entertainment (30%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What RAG does
Retrieval-Augmented Generation, commonly called RAG, combines information retrieval with generative AI. Instead of relying only on a language model’s training data, a RAG application searches approved sources and adds relevant passages to the prompt. This helps produce answers grounded in current, company-specific content.
What it builds
RAG is used for internal knowledge assistants, support search, document question answering and research tools. It can work across policies, product manuals, contracts, technical records and structured business data. Strong solutions define how information is found, cited and presented, not only how text is generated.
- Search private documents and knowledge bases
- Answer questions with source-aware context
- Support multilingual content and terminology
- Connect retrieval to business workflows
Core ecosystem
A RAG project may combine embedding models, vector databases, keyword search and reranking. Common components include OpenSearch, Elasticsearch, Pinecone, Weaviate, Milvus and pgvector, alongside orchestration tools such as LangChain or LlamaIndex. Specialists also work with model APIs, open-weight models, document parsers, metadata stores and evaluation frameworks.
When expertise matters
Companies bring in freelance expertise when a proof of concept must become a dependable product, when search quality is inconsistent or when sensitive data needs careful handling. In Switzerland, projects may involve multilingual source material, regulated industries and collaboration across remote and on-site teams. Clear documentation and secure access design are essential.
- Chunk and enrich complex source documents
- Tune hybrid retrieval and reranking
- Reduce irrelevant or unsupported answers
- Measure retrieval and generation quality
Skills behind reliable systems
Strong professionals understand information architecture as well as language models. They can select embedding strategies, design metadata, manage permissions and trace a response back to its sources. They also know how to test for missing context, prompt injection, stale content and hallucinated claims before release.
Choosing the right specialist
Look for evidence of complete RAG delivery rather than familiarity with a single framework. Ask how the specialist handled document ingestion, access control, evaluation, observability and model changes. A good professional explains trade-offs between semantic, keyword and hybrid search in terms of your data, users and operational constraints.
Frequently asked questions
Not sure where to start with Retrieval-Augmented Generation? These answers cover the essentials.
Retrieval-Augmented Generation is used to create applications that answer questions from trusted external information. Typical examples include internal knowledge assistants, support tools, document search and research workflows. The retrieved context helps the language model respond with information that is more relevant to a company’s own content.
RAG supplies current information at query time, while fine-tuning changes a model’s learned behavior through additional training. RAG is often more suitable when source content changes frequently or must remain traceable. Fine-tuning may help with style, format or specialized behavior, and some systems use both approaches.
A strong Retrieval-Augmented Generation specialist usually understands search systems, embeddings, vector databases and prompt design. Useful adjacent skills include document parsing, data engineering, API integration, cloud security, evaluation and observability. Experience with access permissions is important when answers draw on private business content.
The right level depends on the scope, data quality and risk of the application. A simple prototype may need focused retrieval and prompt expertise, while a production system requires knowledge of ingestion, permissions, testing, monitoring and failure handling. Ask candidates to explain the decisions behind a complete RAG workflow, not only the framework they used.
Retrieval-Augmented Generation work is often suitable for remote collaboration because source data, model access and evaluation workflows can be managed digitally. On-site sessions may still help with security reviews, stakeholder interviews or domain discovery. Agree early on data access, communication routines and the language used for technical and business documentation.
RAG quality can vary across languages because embeddings, tokenization, source structure and terminology differ. Specialists should test retrieval and answer quality for each important language, including mixed-language queries and documents. In Switzerland, this can matter when knowledge bases combine German, French, Italian or English content.
Ask how the specialist would inspect your documents, choose retrieval methods and prove that answers are grounded in approved sources. A capable Retrieval-Augmented Generation professional can discuss citations, access control, prompt injection, stale content and evaluation without hiding behind a tool name. Request a clear plan for a controlled test before a wider rollout.
RAG quality should be assessed in separate retrieval and generation stages. Teams can test whether the right passages are found, whether answers use that context accurately and whether unsupported claims are avoided. A useful evaluation set reflects real user questions, difficult documents, permission boundaries and changes in the underlying knowledge base.
The average hourly rate of freelancers in Switzerland who have used Retrieval-Augmented Generation in their recent projects is 113 €, which corresponds to a daily rate of about 903 € 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, 67% hold at least a Master's degree, and 11% hold a doctorate.
On average, freelancers in Switzerland who have used Retrieval-Augmented Generation in their recent projects have 19 years of professional experience, with a single engagement typically lasting around 2.4 years.
The most common languages among freelancers in Switzerland who have used Retrieval-Augmented Generation in their recent projects are English (100%), German (90%), and French (40%).
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 (70%), and Professional Services (60%).
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 (90%), and Operations (40%).
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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