AI Agents Experts in Zurich
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Meet FRATCH Experts in Zurich, who have recently used AI Agents
Patrick B.
Last position:
Senior Software Engineer at On AG
- Since August 2025 I'm working on the Conversational AI team. We are developing an internal platform for the future of Conversational Ecommerce, powering other teams to use the Agents in their user experience. I'm deeply focused on the architecture of this project, with a particular focus on the monitoring and live evaluation of the agents.
- Played a pivotal role in business-critical operations, including Adyen client upgrades, multi-account configuration for app releases, domain migration support, and implementing a crucial BFCM hotfix to resolve a 10-minute update query (on the checkout workflow) that was blocking sales.
- Served as a key member of the On-Call rotation for over a year, leading >15 incidents, contributing to system stability, and enhancing distributed tracing via New Relic integration, deepening system-wide understanding and operational excellence.
- Conceived and delivered an MVP for a "Low Stock in Key Variants" (LSKV) index within two weeks as part of the Conversion Rate Taskforce, using statistical analysis of size curves to identify OOS products; this initiative led to a (0.9%) CVR improvement post-handover.
- Led major performance and stability initiatives, including a Ruby upgrade (YJIT, Jmalloc) that slashed average response times from 220ms to <100ms, and initiated an "Order Table Cleanup" project to manage 10M+ order records, ensuring SOX compliance and database health.
- Architected and delivered a scalable CloudFlare Worker solution for critical Apple Pay domain validation, mitigating operational risks and establishing a best-practice, automated workflow that was subsequently leveraged by multiple teams, demonstrating cross-functional impact
- Drove substantial evolution of the core e-commerce backend through consistent, high-impact contributions (130+ merged PRs), spanning critical feature delivery, complex system optimizations, and enhancing platform stability."
Yannick H.
Last position:
Managing Director and Trusted Advisor at ODCUS
ODCUS makes sure AI works alongside people in the day-to-day operations of Swiss SMEs while keeping their data secure: vendor-independent, pragmatic and without hidden agendas. We make a company's knowledge useful to AI, automate workflows and protect what matters.
I transform IT from a cost factor into a competitive advantage. As the founder of ODCUS, I advise Swiss SMEs and mid-sized companies—independently, with a focus on results and without hidden agendas.
What drives me: Too many companies invest in technology without knowing its business value. That is where we come in: we always advise on IT in the context of the business and its success.
My focus areas:
- Cybersecurity – Pragmatic protection that fits the business
- Compliance – NIS2, ISO 27001, EU AI Act without excessive bureaucracy
- IT costs – Create transparency, eliminate waste
- AI – From pilot project to measurable value creation
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
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
Discover over 15,000 top freelancers
Statistics of experts using AI Agents
Aggregated from the professional profiles of matched freelancers.
Experience
17 years
Position duration
3.2 years
Positions per freelancer
8
Top business areas
Information Technology, Product Development, Quality Assurance
Top industries
Information Technology, Education, Banking and Finance
Certification focus areas
Information Technology, Project Management, Business Intelligence
Bachelor's degree or higher
100%
Master's degree or higher
50%
Doctorate
17%
Certifications per freelancer
3
Most common languages
English, German, Spanish
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 Zurich 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 Zurich using AI Agents
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.
AI Agents 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%)
- Education (67%)
- Banking and Finance (67%)
- Professional Services (67%)
- Insurance (33%)
- Government and Administration (33%)
- Advertising (17%)
- Aerospace and Defense (17%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What AI Agents Do
AI Agents are software systems that interpret goals, plan actions, use tools, and adjust their next step from results. They combine large language models with prompts, memory, retrieval, APIs, and business rules. Companies use them to automate multi-step work rather than only generate a single response.
What They Build
AI Agents can support customer service, internal knowledge search, document processing, sales operations, IT support, and research workflows. A well-designed agent can decide when to retrieve information, call a service, ask for clarification, or hand a case to a person. In Zurich, these systems can support finance, insurance, healthcare, manufacturing, and professional services while fitting existing operational processes.
Core Ecosystem
The work spans model providers such as OpenAI, Anthropic, Google, and open-source models, plus orchestration libraries and production services. Specialists commonly work with LangChain, LangGraph, LlamaIndex, vector databases, embeddings, structured outputs, REST APIs, Python, TypeScript, and cloud security controls. The right stack depends on data sensitivity, latency, integration needs, and how much control the company requires.
When to Bring in Experts
Companies often need freelance expertise when a prototype must become a dependable product or when internal teams lack experience with agent behavior and evaluation.
- Connect an agent to CRM, ERP, ticketing, or document systems
- Build retrieval-augmented generation with permission-aware access
- Add approvals, guardrails, logging, and human handoffs
- Evaluate quality, latency, cost, and failure recovery
What Strong Specialists Know
Strong professionals treat an agent as a software system, not just a prompt. They define clear tools and boundaries, protect sensitive data, test difficult cases, and make every action observable. They also understand model selection, prompt versioning, context limits, fallback paths, and the difference between a convincing answer and a correct one.
Delivery in Practice
AI Agents projects need close collaboration between technical specialists and people who understand the business process. Remote work can suit discovery, implementation, and evaluation, while on-site sessions may help with workshops, regulated data, or complex operational change. In Zurich, teams may also value professionals who can work in English and coordinate with German-speaking stakeholders. A sound delivery plan starts with a narrow use case, measurable acceptance criteria, secure data flows, and a path to supervised production use.
Frequently asked questions
The facts hiring teams ask for most often when it comes to AI Agents.
AI Agents are used for workflows that require interpretation, decisions, and actions across several systems. Typical examples include support resolution, research, document review, knowledge retrieval, scheduling, and operational task automation.
AI Agents can choose tools, follow a plan, and respond to changing information, while a traditional chatbot usually follows predefined conversation paths. Standard automation is more predictable for fixed rules; agents are useful when inputs are varied but need stronger testing and controls.
A strong AI Agents specialist should understand APIs, data modeling, cloud deployment, identity and access management, observability, and software testing. Experience with retrieval-augmented generation, vector search, prompt design, evaluation, and human approval flows is also valuable.
The right level depends on the risk and scope of the workflow. A contained prototype may need a specialist who can validate the use case, while a production system handling sensitive data needs proven skills in security, evaluation, integration, monitoring, and failure recovery.
AI Agents can connect to CRM, ERP, ticketing, document, database, and communication systems through APIs or controlled tool layers. A specialist should first map permissions, data quality, error handling, and approval points before allowing an agent to take action.
AI Agents work can often be delivered remotely because design, coding, testing, and model evaluation are digital tasks. On-site workshops may still help with process discovery, regulated information, and stakeholder alignment; English is common, while German can support collaboration with local teams.
Ask for evidence of structured evaluations, realistic test cases, secure integrations, useful logs, and clear human handoffs. A good AI Agents implementation measures task success and factual accuracy, not only how natural its answers sound.
Clarify the business outcome, source data, permitted actions, model constraints, ownership of prompts and code, and the process for handling uncertain results. For AI Agents, it is especially important to agree on evaluation criteria, escalation rules, monitoring, and who approves production changes.
The average hourly rate of freelancers in Zurich, Switzerland who have used AI Agents in their recent projects is 121 €, which corresponds to a daily rate of about 967 € based on an 8-hour working day.
Of the freelancers in Zurich, Switzerland who have used AI Agents in their recent projects, 100% hold at least a Bachelor's degree, 50% hold at least a Master's degree, and 17% hold a doctorate.
On average, freelancers in Zurich, Switzerland who have used AI Agents in their recent projects have 17 years of professional experience, with a single engagement typically lasting around 3.2 years.
The most common languages among freelancers in Zurich, Switzerland who have used AI Agents in their recent projects are English (100%), German (83%), and Spanish (50%).
The most common industries among freelancers in Zurich, Switzerland who have used AI Agents in their recent projects are Information Technology (100%), Education (67%), and Banking and Finance (67%).
The most common business areas among freelancers in Zurich, Switzerland who have used AI Agents in their recent projects are Information Technology (100%), Product Development (83%), and Quality Assurance (50%).
Main locations of FRATCH Experts, who have recently used AI Agents
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
