Retrieval-Augmented Generation Experts in Switzerland
, matched fast by AIHire experts who design grounded language applications, connect secure knowledge sources and improve retrieval quality across enterprise data. FRATCH matches you quickly with vetted, available freelancers who fit your technical and business requirements.
Meet FRATCH Experts in Switzerland, who have recently used Retrieval-Augmented Generation
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.
Ralf R.
Last position:
Infrastructure-as-Code Transformation Architect at ODDO BHF
Responsible for designing and delivering a company-wide, comprehensive Infrastructure-as-Code (IaC) transformation in a regulated banking environment.
Conducted an assessment of the current infrastructure, processes, tools, teams, ways of working and skill profiles as a basis for the target architecture and a PDO (Product-Driven Organization)
Defined a tailored, audit-ready Everything-as-Code infrastructure (including governance, security and compliance)
Built a comprehensive IaC operating model, integrating and transforming internal teams
Designed standardised, reusable Terraform modules for infrastructure and platform services
Integrated secrets management, PKI and Encryption-as-a-Service to secure critical VMware and Kubernetes platform components and stored data
Implemented the platform in line with regulatory requirements (DORA, BaFin, ACPR, CRA), with a focus on auditability and traceability
The initiative provides the foundation for standardised, automated and audit-ready infrastructure, reduces manual intervention and enables scalable, traceable delivery of IT resources in a banking environment.
Malik O.
Last position:
AI Scientist at SQPS
- Intelligent Document Processing Engine with OCR & Computer Vision: Architected the end-to-end AI pipeline at the core of the SQPS GenAI document-processing product, which turns scanned paper and PDF batches into organised, searchable documents, combining image pre-processing, OCR and layout analysis for mixed-quality scans. Technologies: Python, OpenCV, Tesseract / PaddleOCR, PyTorch, FastAPI
- Automatic Document Splitting & Classification: Developed models that detect where one document ends and the next begins inside a single multi-document scan, and classify each one by type (invoices, contracts, letters, reports) to enable fully automated filing. Technologies: Python, PyTorch, scikit-learn, Hugging Face Transformers
- Information Extraction & Intelligent File Naming with LLMs: Implemented an LLM-based GenAI extraction system that reads dates, names and reference numbers from unstructured documents and uses them to generate consistent, rule-based file names, with configurable naming conventions per organisation. Technologies: Python, LangChain, OpenAI, SentenceTransformers, SQL
Christian B.
Last position:
5G Campus Product Advisor Telco at German railway group
- Definition of a product concept and end to end solution delivery (turnkey) for a Digital Efficiency Hub for Railway Maintenance based on 5G campus networks. Integration of various hardware and software systems in a comprehensive delivery with success metrics. Laise the concept and process across the enterprise. Compliant process definition and financial management with commercial model.
Technologies: 5G, RAN, SAP, Cloud, Enterprise Architect, Autonomous Robots, ITIL, CoBIT, TBM, BSI, ISO 27001
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
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
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.
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.
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
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
Daniel S.
Last position:
Senior Manager at devpoint GmbH
- Concept development, requirements engineering, and implementation support for a Dubai-based company integrating processes into a CRM
- Business analysis and requirements engineering at Swisscom for the integration of a marketing planning tool at Swisscom
- 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 and process digitization at GIB Solutions AG
- Process designer and prototype developer for an AI-based solution for a real estate property marketing system in Dubai
- Ad interim head of the ICT department at a telecommunications company, reorganizing and optimizing processes with 5 people 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
- Concept, setup, and development of an intelligent refrigerator, IoT, hardware, software
- Setup of devpoint's Nearshore offering ALoHA
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
2.2 years
Positions per freelancer
11
Top business areas
Information Technology, Product Development, Project Management
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
73%
Doctorate
18%
Certifications per freelancer
5
Most common languages
English, German, French
Speak two or more languages
100%
Based on our profile pool as of 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Switzerland 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 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 29 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 (100%)
- Banking and Finance (75%)
- Professional Services (58%)
- Education (42%)
- Healthcare (42%)
- Insurance (33%)
- Automotive (25%)
- Manufacturing (25%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Grounded generation
Retrieval-Augmented Generation, commonly called RAG, combines information retrieval with large language models. Before generating an answer, a RAG system searches approved sources such as documents, databases or knowledge bases and adds relevant context to the prompt. This helps applications answer from current, domain-specific information instead of relying only on model training.
Production use cases
RAG supports internal knowledge assistants, customer service tools and search interfaces that explain their answers. It can work with policies, product documentation, contracts, research material and technical records.
- Enterprise question answering
- Document search with cited sources
- Support and service copilots
- Research and compliance workflows
Retrieval ecosystem
Strong implementations connect ingestion pipelines, document parsers, embedding models and vector databases. Common choices include Elasticsearch, OpenSearch, pgvector, Pinecone, Weaviate and Milvus, alongside cloud storage and relational databases. LangChain and LlamaIndex can support orchestration, but the right design depends on data volume, security and latency needs.
Skills around RAG
Professionals working with RAG understand chunking, metadata, embeddings, hybrid search, reranking and prompt construction. They also know how to manage access control, source freshness, hallucination risks and evaluation datasets. Knowledge of Python, APIs, cloud infrastructure and observability helps move a prototype into a maintainable service.
When companies need expertise
Companies often bring in freelance specialists when a promising language-model prototype gives inconsistent answers or cannot use private information safely. Switzerland-based teams may benefit from experts who can collaborate on site or remotely and work across multilingual content, regulated environments and existing enterprise systems.
- Search results lack relevance or context
- Documents need secure, traceable answers
- A pilot must become a production service
- Retrieval quality needs systematic evaluation
Signs of quality
A strong specialist separates retrieval problems from generation problems and tests both with representative questions. They define useful measures for relevance, groundedness, citation accuracy, response time and cost without treating model output as automatically correct. Clear data boundaries, fallback behavior, monitoring and human review paths are essential to a dependable RAG application.
Frequently asked questions
Not sure where to start with Retrieval-Augmented Generation? These answers cover the essentials.
Retrieval-Augmented Generation is used to create language applications that answer questions from selected, current sources. Typical projects include enterprise search, document assistants, customer support tools and research workflows. The retrieved passages give the language model context it may not have learned during training.
RAG usually changes the information supplied at answer time, while fine-tuning changes model behavior through additional training. RAG is often easier to update when source content changes and can provide citations, whereas fine-tuning may help with style, format or repeated task behavior. A project can use both approaches when their purposes are clearly separated.
A strong Retrieval-Augmented Generation specialist should understand embeddings, vector search, hybrid retrieval, reranking and prompt design. Useful adjacent skills include Python, API integration, data pipelines, cloud security, access control and evaluation design. Experience with LangChain, LlamaIndex, Elasticsearch or pgvector can also be relevant, depending on the stack.
RAG work requires more than connecting a model to a vector database. The right level of expertise depends on data sensitivity, source formats, retrieval complexity, integration scope and production expectations. For a serious engagement, look for evidence of evaluation, monitoring, secure data handling and reliable delivery rather than only a working demo.
Retrieval-Augmented Generation projects can usually be delivered remotely when teams document data access, interfaces, evaluation cases and review decisions. On-site collaboration may help during discovery or when systems contain sensitive information. For Swiss organizations, language expectations should be agreed early when the source material and user questions span German, French, Italian or English.
Evaluate RAG with representative questions and trusted reference answers, not with a few impressive demonstrations. Check whether the system retrieves the right passages, stays grounded in them, cites sources accurately and declines unsupported requests. Also review latency, access boundaries, monitoring and behavior when no relevant information is found.
Poor Retrieval-Augmented Generation results often begin with weak document parsing, unsuitable chunks, missing metadata or an incomplete indexing process. Retrieval can also fail when queries need rewriting, when permissions are ignored or when similar passages are not reranked. Generation prompts and model choice matter, but they cannot fully repair missing or irrelevant context.
A capable RAG professional should leave behind an architecture, ingestion and retrieval components, integration documentation and an evaluation approach. The engagement may also include source citations, access-control rules, monitoring dashboards and operational guidance. Ask how the specialist will handle content updates, failed retrievals, model changes and feedback from users.
The average hourly rate of freelancers in Switzerland who have used Retrieval-Augmented Generation in their recent projects is 108 €, which corresponds to a daily rate of about 866 € 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, 73% hold at least a Master's degree, and 18% hold a doctorate.
On average, freelancers in Switzerland who have used Retrieval-Augmented Generation in their recent projects have 18 years of professional experience, with a single engagement typically lasting around 2.2 years.
The most common languages among freelancers in Switzerland who have used Retrieval-Augmented Generation in their recent projects are English (100%), German (92%), and French (42%).
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 (58%).
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 (92%), and Project Management (50%).
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.
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Philipp Thomaschewski
FRATCH CEO
