Prompt Engineering Experts in Switzerland
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Meet FRATCH Experts in Switzerland, who have recently used Prompt Engineering
Matthias S.
Last position:
Lecturer in Software Management at GraubĂĽnden University of Applied Sciences
- Design and delivery of the university module “Software Management” in the Bachelor’s degree program in Artificial Intelligence in Software Engineering
- Teaching software project management, project organization, stakeholder management, risk and quality management, and agile and hybrid project management
- Coverage of release planning, cut-over, go-live, handover to operations, and the operational impact of technical decisions
- Combining methodological foundations with experience from software development, customer projects, delivery, release management, testing, and IT operations
- Use of AI in software and project management
Methods and tools: Software project management, HERMES, PRINCE2, Scrum, Kanban, hybrid approaches, requirements engineering, risk and quality management, release management, test management, Moodle
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.
Kawahya T.
Last position:
Public Health Expert - Contract at Mercor
- Contributed to Project Atlas as a domain expert supporting the design of realistic, long-horizon professional tasks for AI agent evaluation. Developed complex, public health-relevant workflows that test an AI agent's ability to navigate ambiguous prompts, synthesise information across distributed files and systems, manage multi-step dependencies, handle errors, and produce high-quality professional deliverables.
- Applied public health expertise to design task scenarios involving emergency preparedness, operational readiness, health systems strengthening, risk assessment, surveillance, and policy-oriented decision-making. Responsibilities included creating task prompts, golden solutions, rubrics, and evaluation criteria aligned with real-world professional standards. At the same time, ensuring outputs were action-focused, ethically grounded, and suitable for assessing advanced AI performance in realistic organisational environments.
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
Ahmed R.
Last position:
AI & Automation Engineer at Teosek GmbH
- Build prototypes, expand AI skills, and practical application in a startup context.
- Transition phase: further training in prompt engineering, APIs, AI, working on independent prototype projects, and collaborating at a friend's startup.
- Technologies: LangChain, REST, Python, Java, JS, TS, Node.
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.
Csaba K.
Last position:
ServiceNow Consultant at DXC
- Worked in mixed teams with several clients
- Performed requirement engineering, proposal and time estimation
- Developed ACLs, business rules, flows, workflows, catalog items, various scripts, UI actions, policies, scoped applications
- Customized mobile apps: Now Agent and Now Mobile
- Implemented integrations using the Integration Hub
- Coordinated continuously with clients during development
- Performed testing and helped clients with testing
- Deployed to production and applied main version upgrades and patches
- Managed CMDB, domain separation and asset management
- Integrated between instances at different clients
- Developed GlideScript: business rules, scripted REST endpoints, extension points, fix scripts, background scripts
Klaus F.
Last position:
Freelancer at 3P Quality Consulting
Data integrity training (basic and advanced training content)
Root cause analysis and CAPA courses for 40+ employees
Risk-Management system design for quality oversight
SOP writing excellence, including use of AI tools (prompting)
Implementation of an audit remediation program as person in the plant (PIP)
Director of a quality assurance department (6 direct reports), budgeting
Development of use cases for ChatGPT in GxP context (6 months, part-time)
Reengineering of processes for a MA holder of generic products, valued operational improvements (Germany, Spain)
eQMS and document management system: delivery of the business case and investment recommendation
Analytical method validation, process and computer system validations, batch record review, PQR reviews
Articles in international journals and LinkedIn on modular QMS-design, risk-management in the supply chain, knowledge management, use of AI for compliance improvements
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
Sigrid A.
Last position:
CAS Autism and ADHD Coach
- Writing in plain and autism-friendly language
- Participation in research projects on autism
- Lectures and collaborations with international autism experts
- Translations
- Volunteer work on the board of the umbrella association 'Neurodiversity' and as lead of the Work and Career specialist group with responsibilities: public relations and communication, project management, study coordination, newsletter editing, web design and website maintenance, content creation, social media, concept development and project coordination, creating university courses, workshops and seminars, and designing informational brochures.
Discover over 15,000 top freelancers
Statistics of experts using Prompt Engineering
Aggregated from the professional profiles of matched freelancers.
Experience
20 years
Position duration
2.6 years
Positions per freelancer
10
Top business areas
Information Technology, Product Development, Quality Assurance
Top industries
Information Technology, Banking and Finance, Healthcare
Certification focus areas
Information Technology, Project Management, Product Development
Bachelor's degree or higher
92%
Master's degree or higher
67%
Doctorate
25%
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 Prompt Engineering
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.
Prompt Engineering 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 (77%)
- Banking and Finance (46%)
- Healthcare (46%)
- Professional Services (46%)
- Education (38%)
- Government and Administration (38%)
- Insurance (23%)
- Retail (23%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Prompt Engineering Covers
Prompt Engineering is the structured design, testing, and refinement of instructions for large language models and other generative AI systems. It turns business goals into prompts that guide models toward useful, consistent, and safe outputs. The work can support text, image, audio, or multimodal applications.
What It Builds
Prompt Engineering appears in customer support assistants, research tools, document workflows, content systems, and internal knowledge applications. Strong prompts define context, constraints, output formats, and fallback behavior rather than relying on vague requests.
- Structured prompts for classification, extraction, and generation
- Retrieval-augmented workflows grounded in company information
- Prompt chains for multi-step reasoning and automation
- Evaluation sets for accuracy, relevance, safety, and consistency
Ecosystem and Tools
Professionals work with models and services such as OpenAI, Azure OpenAI, Anthropic Claude, Google Gemini, and open-weight models. They may use LangChain, LlamaIndex, vector databases, orchestration tools, observability suites, and API integrations. Useful adjacent skills include Python, JavaScript, data preparation, and cloud security.
When Companies Need Experts
Companies often bring in freelance expertise when a prototype produces uneven results, domain knowledge is not reflected in outputs, or prompt changes are difficult to govern. In Switzerland, projects may involve multilingual content, regulated industries, or collaboration between local stakeholders and remote product teams.
- Moving a generative AI proof of concept toward production
- Reducing hallucinations and improving source grounding
- Creating reusable prompt templates and version control
- Establishing evaluation and review processes
What Strong Professionals Deliver
Strong professionals translate user needs into testable prompt behavior. They compare model responses, document assumptions, create representative evaluation cases, and distinguish prompt issues from data, retrieval, or model limitations. They also account for privacy, access control, injection risks, and human review.
Choosing the Right Specialist
Ask for examples that show measurable improvements in consistency, not only polished demonstrations. Discuss the target models, languages, data sources, integration boundaries, and ownership of prompts and evaluations. Remote collaboration works well when requirements, test cases, review cycles, and security responsibilities are clearly documented; on-site work can help when workshops involve sensitive processes or many stakeholders.
Frequently asked questions
Questions about Prompt Engineering? Start with the answers below.
Prompt Engineering is used to guide generative AI systems toward reliable outputs for tasks such as summarization, classification, extraction, drafting, and question answering. It can also define how a model uses retrieved information, handles uncertainty, and formats results for another system.
Prompt Engineering changes the instructions, context, examples, and workflow around a model without changing its underlying weights. Fine-tuning can be better when a stable behavior must be learned from a substantial, well-prepared dataset, while prompting is often easier to revise and test.
A strong Prompt Engineering specialist often combines model knowledge with data analysis, API integration, retrieval-augmented generation, and evaluation design. Familiarity with Python or JavaScript, vector search, cloud security, and user research is valuable when prompts are part of a larger product.
A Prompt Engineering project needs enough practical experience to cover its risk and integration depth, not a fixed amount of time. A simple internal workflow may need focused prompt and evaluation work, while a customer-facing or regulated system calls for proven testing, monitoring, privacy controls, and fallback design.
Prompt Engineering is well suited to remote collaboration when stakeholders can share representative data, test cases, and review feedback securely. Swiss projects may also require local workshops for sensitive processes, multilingual requirements, or alignment across business and compliance teams.
Assess Prompt Engineering through a repeatable evaluation set rather than a single impressive response. Look for documented criteria covering factuality, relevance, tone, safety, latency, and failure handling, along with clear comparisons between prompt, retrieval, data, and model changes.
Prompt Engineering is a sensible first step when the selected model already has the required general capability but lacks context, structure, or clear constraints. A model change may be more appropriate when core language coverage, reasoning behavior, latency, or data handling cannot be improved through instructions and workflow design.
A typical Prompt Engineering deliverable includes versioned prompts, input and output schemas, representative test cases, evaluation results, and guidance for integration. For production work, it should also explain model settings, retrieval sources, safety measures, monitoring signals, and how future changes will be reviewed.
The average hourly rate of freelancers in Switzerland who have used Prompt Engineering in their recent projects is 119 €, which corresponds to a daily rate of about 954 € based on an 8-hour working day.
Of the freelancers in Switzerland who have used Prompt Engineering in their recent projects, 92% hold at least a Bachelor's degree, 67% hold at least a Master's degree, and 25% hold a doctorate.
On average, freelancers in Switzerland who have used Prompt Engineering in their recent projects have 20 years of professional experience, with a single engagement typically lasting around 2.6 years.
The most common languages among freelancers in Switzerland who have used Prompt Engineering in their recent projects are English (100%), German (92%), and French (38%).
The most common industries among freelancers in Switzerland who have used Prompt Engineering in their recent projects are Information Technology (77%), Banking and Finance (46%), and Healthcare (46%).
The most common business areas among freelancers in Switzerland who have used Prompt Engineering in their recent projects are Information Technology (85%), Product Development (85%), and Quality Assurance (69%).
Main locations of FRATCH Experts, who have recently used Prompt Engineering
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
