Large Language Model Experts in Zurich
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Meet FRATCH Experts in Zurich, who have recently used Large Language Model
Vincent Verdet
Last position:
IT Business Partner & Enterprise Architect at Sulzer
- Established the Enterprise Architecture function from the ground up for Sulzer Group (~13,500 employees, CHF 3.6B revenue)
- Defined the Technical Governance framework, Target Operating Model, and IT PMO foundation now governing architecture decisions and portfolio demand across all divisions.
- Deployed SAP LeanIX as the authoritative application portfolio system of record - catalogued 500 applications group-wide (140 for Chemtech), onboarded business and IT stakeholders, and initiated integration with ServiceNow to embed architecture data into operational workflows.
- Led the divisional & group wide Application Rationalization initiative
- Decommissioned 15% of the 140 applications Chemtech portfolio, delivering CHF 627k in run-rate savings over two years and materially reducing licensing and support exposure.
- Defined and executed the divisional AI Roadmap in partnership with Sales, Legal, and R&D leadership
- Delivered 4 AI solutions into production (including Sales process harmonization and LLM-based IP conflict detection) from a portfolio of 7 prioritized initiatives.
- Built and owned the 3-year IT Strategic Roadmap for Chemtech
- Aligned cloud and technology architecture with divisional business priorities and a CHF 1.5M annual investment envelope.
- Delivered CHF 500k in cost avoidance by challenging and discontinuing funded but low-value initiatives, demonstrated governance discipline and executive influence, with zero implementation spend.
Matthias Sauer-Rank
Last position:
Lecturer Software Management, 10% at FH Graubünden
- Design and delivery of the university module "Software Management" in the Bachelor's program Artificial Intelligence in Software Engineering
- Teaching software project management, project organization, stakeholder management, risk and quality management, as well as agile and hybrid project steering
- Covering release planning, cut-over, go-live, handover to operations, and the operational impact of technical decisions
- Developing a case study across the semester for practical application of the methods taught
- Designing lectures, group work, risk and decision workshops, and practice-oriented assessments
- Combining methodological fundamentals with experience from software development, customer projects, delivery, release, testing, and IT operations
- Use of AI in software and project management
Methods and tools: Software project management, HERMES, PRINCE2, Scrum, Kanban, hybrid delivery models, requirements engineering, risk and quality management, release management, test management, Moodle
Gwang Jin Kim
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
Robin Ochsner
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
Daniel Schmidt
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
Matthias Isler
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.
Georgios Sfakianakis
Last position:
Senior Software Engineer at UBS Bank
- Implementations of a code refactoring framework able to refactor thousands of repositories leveraging Generative AI
- Use Python (Django, Flask, FastAPI), Java and Typescript in Azure Cloud (Data Lake, VMs, AI) and GitLab infrastructure
- Mentoring and pair programming
- Obtained Azure AI-900 and AI-102 certifications
Karl Estermann
Last position:
incl. CI/CD, automation at AALS Software AG
- Designed and delivered a practical real-time course on Flink and Hadoop with MapReduce, HDFS, Spark, Flink, Hive, HBase, MongoDB, Cassandra, and Kafka
- Gained extensive DevOps and CI/CD experience
- Created ETL/ELT pipelines with Apache tools and Pentaho
- Led projects in municipal software, financial services, and big data with Kafka
- Developed AI/NLP models and chatbots with RASA, Chatter, and Dialogflow
- Built and managed a TypeDB knowledge database
- Worked with OpenStack, Kubernetes, and Podman
Discover over 15,000 top freelancers
Statistics of experts using Large Language Model
Aggregated from the professional profiles of matched freelancers.
Experience
23 years
Position duration
2.9 years
Positions per freelancer
8
Top business areas
Information Technology, Product Development, Project Management
Top industries
Information Technology, Banking and Finance, Education
Certification focus areas
Information Technology, Project Management, Business Intelligence
Bachelor's degree or higher
100%
Master's degree or higher
75%
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 30 Aug 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 Large Language Model
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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
What it is
A large language model is a system trained on text to understand and generate natural language. It powers chat interfaces, document search, summarization, extraction, classification, and content drafting. Teams also use LLMs as the language layer behind copilots and internal assistants.
Common work
- Prompt design and testing
- Retrieval-augmented generation, or RAG
- Fine-tuning and model evaluation
- Guardrails, safety, and output controls
- API integration into existing products
Tooling and stack
Strong specialists know the model APIs, embedding workflows, vector databases, and evaluation methods that keep results reliable. They also work with orchestration frameworks, logging, and prompt versioning to make changes traceable. In Zurich, they often support teams that need English outputs with careful handling of German and multilingual input.
When to bring in help
Companies bring in freelance expertise when they need to move from demo to production, unblock a difficult use case, or compare models without locking into one vendor. This is common when the scope includes search over private knowledge, document-heavy workflows, or customer-facing assistants. A good specialist keeps the project focused on useful behavior, not novelty.
What strong specialists do
- Define the task clearly and measure output quality
- Choose between prompting, RAG, and fine-tuning
- Reduce hallucinations and fragile responses
- Fit the model to privacy and access rules
- Document trade-offs for product and engineering teams
Zurich project fit
Zurich companies often need LLM support for finance, insurance, legal, consulting, life sciences, and enterprise software. The work may be on-site for stakeholder workshops, then remote for build and testing. Clear communication matters, especially when product teams, domain experts, and language requirements all meet in one project.
Frequently asked questions
Key details about Large Language Model, drawn from the questions we get asked most.
A strong large language model specialist helps turn text-heavy work into a usable system. That can mean prompt design, chat assistants, document Q&A, summarization, extraction, or support for internal knowledge search. The best specialists also define quality checks so the output stays useful after launch.
LLM work is a major part of generative AI, but not all generative AI work is text based. Large language models focus on natural language, while other generative systems may create images, audio, or code in different ways. If your use case is about reading, writing, or searching text, an LLM specialist is usually the right starting point.
A large language model project often starts with RAG when the model needs current or private information from your own documents. Fine-tuning makes more sense when you need consistent style, format, or domain-specific behavior that prompting alone cannot deliver. Many teams use both, but a good specialist will test the simpler path first.
Look for LLM specialists who also understand API integration, search, embeddings, vector stores, and evaluation. For enterprise work, privacy, access control, and logging matter just as much as prompt quality. In Zurich, multilingual handling can also be important when teams work across English and German.
A large language model project does not need a full research team to get started, but it does need someone who can separate a useful pilot from a fragile prototype. If the work touches customers, internal policy, or sensitive data, bring in help early. That avoids expensive rework once the first version is already in use.
Yes, LLM work is often well suited to remote collaboration because prompts, evals, and integration tasks are easy to review in shared tools. On-site time can still help at the start, especially for workshops, stakeholder alignment, or access to internal systems. Many Zurich teams use a mix of both.
A good large language model specialist shows clear test cases, failure modes, and measurable review steps. Look for evidence of evaluation on real inputs, not just impressive demos. The work should also explain when the system should refuse, escalate, or cite sources instead of guessing.
GPT is a specific family of large language models, while the term large language model covers a wider category. In practice, people may say LLM, GPT, or generative AI when they mean text models used for chat, writing, or retrieval. A strong specialist should know when the brand name matters and when the underlying capability is the real issue.
The average hourly rate of freelancers in Zurich, Switzerland who have used Large Language Model in their recent projects is 122 €, which corresponds to a daily rate of about 972 € based on an 8-hour working day.
Of the freelancers in Zurich, Switzerland who have used Large Language Model in their recent projects, 100% hold at least a Bachelor's degree, 75% hold at least a Master's degree, and 25% hold a doctorate.
On average, freelancers in Zurich, Switzerland who have used Large Language Model in their recent projects have 23 years of professional experience, with a single engagement typically lasting around 2.9 years.
The most common languages among freelancers in Zurich, Switzerland who have used Large Language Model in their recent projects are English (100%), German (88%), and French (38%).
The most common industries among freelancers in Zurich, Switzerland who have used Large Language Model in their recent projects are Information Technology (88%), Banking and Finance (75%), and Education (50%).
The most common business areas among freelancers in Zurich, Switzerland who have used Large Language Model in their recent projects are Information Technology (100%), Product Development (75%), and Project Management (75%).
Main locations of FRATCH Experts, who have recently used Large Language Model
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
