Large Language Model Experts in Zurich
matched in minutes from over 15,000 CVsHire experts who design reliable LLM workflows, connect GPT models to business data, and ship retrieval-augmented applications. FRATCH matches you quickly with vetted, available freelancers whose skills fit your project.
Meet FRATCH Experts in Zurich, who have recently used Large Language Model
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
Vincent V.
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.
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
Daniel S.
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
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
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.
Georgios S.
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
Alejandro A.
Last position:
AI Researcher & Engineer at Tufa Labs
- Deployed and optimized the inference stack on a multi-node DGX B200 cluster across vLLM and SGLang (serving, throughput and latency tuning).
- Built, with a small team, an internal Python library for LM pretraining covering the full training loop: distributed training with PyTorch FSDP, data pipelines, checkpointing, config and hyperparameter management, and experiment tracking.
- Built and evaluated agent scaffolds on interactive game benchmarks similar to ARC-AGI-3, with metrics for how models plan, explore and adapt across multi-step episodes; classified model errors and fed the findings back into scaffold and evaluation design.
- Researched looped transformer architectures.
Karl E.
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
21 years
Position duration
2.5 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
82%
Doctorate
18%
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 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 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Large Language Model 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 (91%)
- Banking and Finance (82%)
- Education (55%)
- Professional Services (36%)
- Government and Administration (27%)
- Aerospace and Defense (18%)
- Automotive (18%)
- Biotechnology (18%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What LLMs do
A Large Language Model, often called an LLM, processes and generates natural language from patterns learned across large text collections. Companies use it for conversational assistants, document analysis, content workflows, semantic search, translation and structured data extraction. GPT is a widely known model family within this field.
Products and workflows
LLM projects can support customer service, internal knowledge access, sales operations and software interfaces. Strong implementations define a clear task, control the model’s context and connect responses to business processes rather than treating generated text as a finished product.
- Conversational assistants grounded in company knowledge
- Retrieval-augmented generation for search and question answering
- Document classification, summarisation and extraction
- Prompt-based automation with human review
Ecosystem and tooling
The work often combines model APIs from OpenAI, Anthropic or Google with Python or TypeScript services, vector databases and orchestration tools such as LangChain or LlamaIndex. It may also involve open-weight models, Hugging Face, embeddings, evaluation suites, observability and cloud deployment.
When specialists help
Companies bring in freelance expertise when a proof of concept must become a dependable product, internal data needs safe retrieval, or model costs and response quality need control. Zurich teams may value on-site workshops for discovery while keeping implementation remote. Clear English and, where needed, German language handling should be agreed early.
- A prototype gives inconsistent or unverifiable answers
- Business data must be indexed with permissions and traceability
- Prompts, models and retrieval need systematic evaluation
- An LLM feature must integrate with existing systems
Skills that matter
Good professionals understand language modelling and also know data pipelines, API design, security, privacy, testing and product discovery. They can choose between prompting, retrieval, fine-tuning and conventional software, then explain the trade-offs in terms of accuracy, latency, maintainability and operational risk.
Quality in delivery
Strong LLM work includes representative test sets, documented prompts, source citations where appropriate and safeguards against prompt injection and data leakage. Professionals monitor failures after launch, manage model and provider changes, and create handover material so teams can maintain the workflow without guesswork.
Frequently asked questions
Key details about Large Language Model, drawn from the questions we get asked most.
A Large Language Model can power assistants, document search, summarisation, classification, extraction and natural-language interfaces. The best use cases have clear source data, measurable response quality and a defined human review path for sensitive outputs.
An LLM can interpret natural-language questions and compose an answer, while traditional search mainly retrieves matching records or documents. Retrieval-augmented generation combines both, but the underlying search, permissions and citations still need careful design.
A Large Language Model specialist should usually understand API integration, Python or TypeScript, data preparation, vector search and cloud operations. Security, privacy, evaluation design and user experience are equally important when the model supports a business process.
A Large Language Model may need fine-tuning when a stable style, classification behaviour or specialised output format cannot be achieved reliably through prompting and retrieval. Retrieval is usually more suitable when the main challenge is keeping answers aligned with changing company knowledge.
An LLM proof of concept may need a focused specialist who can test the use case and its data quickly. A production system needs broader experience with evaluation, security, integration, monitoring and failure handling, especially when outputs affect customers or operations.
An LLM project can usually be delivered remotely when data access, decision owners and review routines are clear. Zurich companies may still prefer on-site discovery or workshops, while implementation, testing and documentation take place across a distributed team.
A Large Language Model solution should be tested against representative tasks, expected sources and known failure cases. Look for grounded answers, consistent formats, useful refusal behaviour, traceability, acceptable response times and monitoring after release.
A Large Language Model professional may work with GPT models, Claude, Gemini or open-weight models hosted through services such as Hugging Face. The right choice depends on data controls, language coverage, context needs, integration options and the project’s operating requirements.
The average hourly rate of freelancers in Zurich, Switzerland who have used Large Language Model in their recent projects is 117 €, which corresponds to a daily rate of about 937 € 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, 82% hold at least a Master's degree, and 18% hold a doctorate.
On average, freelancers in Zurich, Switzerland who have used Large Language Model in their recent projects have 21 years of professional experience, with a single engagement typically lasting around 2.5 years.
The most common languages among freelancers in Zurich, Switzerland who have used Large Language Model in their recent projects are English (100%), German (82%), and French (45%).
The most common industries among freelancers in Zurich, Switzerland who have used Large Language Model in their recent projects are Information Technology (91%), Banking and Finance (82%), and Education (55%).
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 (82%), and Project Management (64%).
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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