Prompt Engineering Experts in Zurich
in minutes from over 15,000 CVs with the power of AI.Hire experts who design effective prompts, build reusable prompt templates, and tune LLM workflows for chatbots, search, content automation, and internal tools. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Zurich, who have recently used Prompt Engineering
Matthias S.
Last position:
Lecturer in Software Management at Graubünden 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.
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
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.
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
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
100%
Master's degree or higher
86%
Doctorate
14%
Certifications per freelancer
4
Most common languages
German, English, Spanish
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 Zurich 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 Zurich 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 (100%)
- Banking and Finance (71%)
- Healthcare (43%)
- Professional Services (43%)
- Government and Administration (43%)
- Advertising (29%)
- Aerospace and Defense (29%)
- Education (29%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Prompt design
Prompt engineering turns a language model into a reliable work tool. It focuses on instructions, examples, output format, and guardrails so the model responds in the right way. Strong prompt work improves chat assistants, content workflows, retrieval tasks, and structured data extraction.
Common use cases
- Customer support copilots and internal knowledge assistants
- Content drafting, rewriting, and classification flows
- Summaries, extraction, and report generation
- Tool-using agents and workflow automation
- Evaluation prompts for testing model behavior
What experts do
Experienced specialists write prompts that are clear, robust, and repeatable. They compare model behavior, reduce hallucinations, and shape outputs for downstream systems. In Zurich, this often supports teams in finance, insurance, consulting, and software products that need careful language control.
Ecosystem skills
Prompt engineering is rarely isolated. Strong professionals usually understand LLM APIs, system prompts, few-shot examples, function calling, retrieval-augmented generation, and evaluation methods. They also work with tools such as OpenAI models, Anthropic Claude, and open-source LLM stacks when the project needs them.
When to bring in help
Bring in freelance expertise when prompts become hard to maintain, outputs vary too much, or a prototype must move into production. It also helps when a team needs better structure for multilingual use, sensitive content, or human review. Zurich companies often use remote specialists for speed, while on-site workshops can help align product, legal, and operations teams.
What strong professionals deliver
A strong prompt specialist does more than write clever instructions. They create testable prompt sets, document assumptions, define edge cases, and hand over clear guidance for teams that will maintain the work. Good specialists also know when prompt changes are enough and when the system needs retrieval, model changes, or workflow redesign.
Frequently asked questions
Everything clients usually want to know about Prompt Engineering, in one place.
Prompt engineering covers the way instructions, examples, context, and output rules are written for a language model. The goal is to make responses more useful, consistent, and safe for a real task. It is used for chatbots, extraction, summarization, classification, and agent workflows.
Prompt engineering is the broader term most teams use when prompts are treated as part of a system, not just a line of text. Prompt design and prompt writing often describe the same work, but they can sound narrower or less technical. In practice, companies usually care about the result: reliable model behavior.
You should bring in Prompt Engineering help when prompts are inconsistent, hard to scale, or hard to test. It is also useful when a team is moving from a demo to production or needs better control over tone, format, or compliance-sensitive output. A specialist can turn ad hoc prompting into something the team can maintain.
A strong prompt engineering professional usually understands LLM APIs, retrieval-augmented generation, evaluation, and basic product thinking. Knowledge of function calling, structured output, and data labeling is also valuable. For Zurich teams, clear communication in English and often German can help with cross-functional work.
Prompt engineering changes the instructions and context you give a model, while fine-tuning changes the model itself. Many teams start with prompting because it is faster to test and easier to revise. Fine-tuning makes sense when prompts are no longer enough for stable performance or domain style.
A Prompt Engineering project can start with a specialist who knows the model family, the task, and how to test outputs. Simple prototypes need less depth than systems with retrieval, guardrails, or multi-step agents. For production work, you want someone who can explain trade-offs and document the prompt logic.
Yes, prompt engineering is often a good fit for remote work because most of the task lives in text, tests, and feedback loops. In Zurich, many companies combine remote delivery with short on-site sessions for product alignment or stakeholder review. That works well when the scope is clear and review cycles are fast.
Look for evidence that Prompt Engineering work is testable, documented, and tied to a real business task. Good specialists show prompt versions, edge cases, failure analysis, and clear acceptance criteria. They should also know when to improve the prompt and when the underlying workflow needs a different solution.
The average hourly rate of freelancers in Zurich, Switzerland who have used Prompt Engineering in their recent projects is 111 €, which corresponds to a daily rate of about 889 € based on an 8-hour working day.
Of the freelancers in Zurich, Switzerland who have used Prompt Engineering in their recent projects, 100% hold at least a Bachelor's degree, 86% hold at least a Master's degree, and 14% hold a doctorate.
On average, freelancers in Zurich, 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 Zurich, Switzerland who have used Prompt Engineering in their recent projects are German (100%), English (100%), and Spanish (29%).
The most common industries among freelancers in Zurich, Switzerland who have used Prompt Engineering in their recent projects are Information Technology (100%), Banking and Finance (71%), and Healthcare (43%).
The most common business areas among freelancers in Zurich, Switzerland who have used Prompt Engineering in their recent projects are Information Technology (100%), Product Development (86%), and Quality Assurance (86%).
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.
Countries:
Zurich 
Geneva Basel Bern Request a free demo
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Philipp Thomaschewski
FRATCH CEO
