Large Language Model Experts in Frankfurt
matched in minutes by AIHire experts who design language-powered applications, retrieval systems and reliable AI workflows with models such as GPT, Claude and open-source LLMs. FRATCH matches you quickly with vetted, available freelancers who fit your technical and business requirements.
Meet FRATCH Experts in Frankfurt, who have recently used Large Language Model
Patrick L.
Last position:
Senior GenAI Fullstack Developer at SBH (Schulbau Hamburg)
Remote freelance role focused on Agentic AI strategy, secure application patterns, and reusable agentic workflows for a government agency.
- Development and implementation of an open-source Agentic AI strategy for a government agency, with a focus on GDPR, security, and self-hosted solutions
- Development of reusable agentic workflows and business applications that enable non-technical employees to solve business problems independently
- Implementation of nine business applications with Single Sign-On (SSO) and Azure PostgreSQL integration on Hetzner Linux servers
Techstack: Python, Streamlit, Anthropic SDK (Claude), Azure, Linux, PostgreSQL, MS SQL, Angular
Alexander H.
Last position:
Senior Product & Strategy Consultant at hegehofer.consulting
Design, development, and market launch of digital products and platforms – from strategic alignment and agile implementation to Go-to-Market and Go-Live, integrating AI into processes and products
Ornel Franck W.
Last position:
Sales Partner at ERGO PRO
- Industry: Insurance, Trade, IT
- Customers & Projects: Consulting and selling insurance and similar services
- Main tasks: Insurance consulting (health insurance, retirement planning, wealth building); commercial services, inside and field sales; sales data analysis and forecasting; customer consulting and support; opening a new sales headquarters for private and business customers; business development & innovation management; business use case development; process optimization; stakeholder management; team leadership and training; preparation and delivery of trainings;
- Technologies used: Microsoft Office 365, Microsoft Teams, Jira, Draw.IO, Camunda 8, Java (8, 17,21,25), Git, Spring Boot, Spring Batch, Spring Data REST, Spring Web, Spring Security, J-Unit, Playwright, Lombock, Vaadin, H2, PostgreSQL (16, 17 18), pgAdmin, Docker, LLMs, JasperSoft Studio, JasperReports
Saqib J.
Last position:
AI Developer / AI Engineer (Lead) at KOM4TEC GmbH
- Designed and implemented modular AI assistants for sales and business processes within the Microsoft ecosystem (Agentic AI, Copilot extensions)
- Frontend architecture and development with React + TypeScript for embedded chat and assistant surfaces (streaming UI, Hooks, React Query, OpenAPI clients)
- Enterprise-level agent development: reusable skill/agent library, MCP servers, review and compliance gates
- LLM integration into the user experience: Anthropic (Claude), OpenAI, tool use, RAG pipelines, prompt engineering, guardrails
- Architecture and code review consulting, and mentoring within the AI development team
- Integration with Microsoft Graph, Power Platform, and Azure services
- Technologies: React, TypeScript, Anthropic Claude, OpenAI, MCP, RAG, Microsoft Graph, Power Platform, Azure
Ali A.
Last position:
Founder & Architect at Independent AI R&D
- Fully on-premises LLM document-examination platform for a compliance-critical banking domain: agentic LangGraph pipeline with deterministic verification, every AI judgment structured and source-anchored; ~960 automated tests, zero data egress
- GPU throughput engineering (quantized serving, speculative decoding, prefix caching): 9.5x extraction speed-up, 500+ multi-document case files per day on a single A100
- AI-native EDI/EDIFACT integration platform (~116k LOC Java 25 / Spring Boot 4, 1,900+ tests): LLM-drafted partner mappings machine-verified before go-live (DFDL conformance, field-coverage checks, dry runs), ~99.5% byte match on real customer files — replacing weeks of manual mapping per partner
Anthony M.
Last position:
Research and Development, AI for Enterprise at Mwanachama
- Built an MCP (Model Context Protocol) layer for Mwanachama's agency service, turning domain manager methods into callable AI-agent tools. This included a composite tool that builds a full organization design (org chart, goals, workflows, RACI matrix) from one specification.
- Built the chat-driven agency builder (Wakala Studio and API), where an organization describes its structure in natural language and an AI agent uses those tools to construct and modify the live design.
- Added an insights service so an organization can review AI-agent interactions and completed work. Insights from that review feed back into solution design, gated by architect and user sign-off.
- Alongside this, designed and built the platform itself: ~20 Go microservices on PostgreSQL, Flutter and React clients, deployed on Kubernetes.
- AI agents scan the platform autonomously for security gaps and run scripted tests, covering API (Postman-style) and UI testing. The rest of the work stays supervised. No rogue agents, promise.
- Building an AI-native enterprise platform that structures organizational data, workflows and responsibilities for both people and AI agents.
- Models accountability through RACI, allowing human and AI participants to take different operational roles, including Responsible, Accountable, Consulted and Informed. AI agents can also take controlled approval roles where the organization assigns that responsibility.
- Extended the organizational and operational-risk themes from my Executive MBA thesis into software, making roles, processes and controls explicit and machine-readable.
- Structures enterprise context so LLM interactions use more relevant information instead of repeatedly processing large amounts of unstructured content.
Max B.
Last position:
Customer Portal (Telecommunications) at Freelance Senior UX Architect
- Drove key experience streams in the redesign of a large-scale telecommunications e-commerce platform focusing on mobile plan selection, product configuration, and digital contract completion.
- Delivered scalable Figma-based white-label solutions within a multinational Scrum setup to enable efficient multi-brand rollout.
Prasad T.
Last position:
Solution Architect / Senior Manager – DTC E-Commerce Platform at BRITA
- Led discovery phase and POC for Shopware to Shopify Plus migration across EMEA markets, evaluating platform suitability, technical architecture, and multi-brand/multi-country capabilities against business requirements.
- Designed reference architecture for Shopify Plus implementation incorporating headless front-end patterns (Vue.js, Nuxt.js), CMS integration (Magnolia), and Azure middleware (APIM, Functions, Logic Apps, Service Bus) for 11 EMEA markets.
- Defined migration strategy analyzing data mapping, cutover approach, and zero-downtime deployment patterns using Varnish caching, GitOps pipelines, and CI/CD orchestration across six vendor teams.
- Architected multi-tenant Shopify Plus governance model with centralized admin, localized storefront customization, and compliance controls (GDPR, data residency).
- Prototyped AI-driven search optimization (LLM.txt, JSON-LD) for product discoverability in Google AI results, demonstrating post-launch performance opportunities.
- Defined EMEA expansion roadmap for 15+ markets through C-level strategic workshops, identifying phased rollout, market-specific configurations, and resource requirements.
- Tech Stack: React, Nuxt.js, Vue.js, Magnolia CMS, Shopware, Shopify Plus, Azure (APIM, Functions, Logic Apps, Service Bus, Front Door), Varnish, SAP, MS Dynamics, Docker, Kubernetes, GitHub Actions, PostgreSQL, Kafka
Minh D.
Last position:
Project Manager / Business Analyst / Application Manager at Finance and Insurance
Introduction of 5 different process applications for different teams
Release planning: scope and time management
Resource/capacity planning
Conducting Sprint Planning / retrospectives
Increment planning (several sprints)
Preparation of Steering Committee meetings / reporting to the Board
Coordination / alignment with external suppliers / deliveries
Resource planning for multiple projects
Coordination with business department and development team
Identification of best practices with IBM BAW
Cost control and planning for the project team and external service providers
Collection of KPIs using LogScale
Analysis of application errors with LogScale / Queries
User Story definitions / coordination of requirements with the business department and development team
Testing and defect tracking
UI/UX design of applications
Preparation and moderation of Brown-Paper workshops
Test concept, test data, test organization, test execution
Collection of team velocity / metrics
Test execution
Script for automated testing
Test organization with business department and IT
Recording and prioritization of defects
Establishment and operation of the application
Establishment of application monitoring with LogScale Dashboards
Checking health endpoints with PowerShell
Post Mortem analysis
Establishment of Incident Management
Establishment of Problem Management
Analysis of errors using LogScale Queries and Dashboard
Pre-processing of data for AI
Evaluation using AI language models (Meta Llama 3.3 LLM and deepset Haystack) and RAG
Installation of runtime environments for LLMs (Large Language Model)
Evaluation of various LLMs
Installation of RAG (Retrieval Augmented Generation) and integration with LLM
Extraction of unstructured data with LLM and RAG
Project based on IBM BAW (Business Automation Workflow), Websphere Liberty, Domea, d.3, REST, LogScale (formerly Humio), Swagger, Powershell, JIRA, Confluence, Lucom Interaction Platform (LIP), Mattermost, Jabber
Eduard V.
Last position:
Lead of workshop “Introduction to AI Development Tools” at Software company in Wiesbaden
- Presentation introducing generic A.I. and Large Language Models
- Explanation of legal frameworks (EU AI Act, US CLOUD Act, DSGVO)
- Systematic review of AI tools along the SDLC and holistic systems
- Comparison of on-prem LLMs vs. cloud-based solutions, as well as change management and works council considerations
- Moderation of the discussion and definition of next steps for introducing AI development tools
Nils G.
Last position:
Sales Trainer at Self-employed
- Teaching sales techniques, automations, and body practices to increase acquisition, conversion rates, and objection handling.
- Created a 5-step transformation for freelancers based on 20+ feedbacks and 3 1-on-1 coachings.
- Developed, tested, and iterated a sales training system with AI.
Mathew D.
Last position:
Data Science Expert and AI Strategist at Freelancer
- Built an API to ingest, clean, translate, and index EU tenders documents in Neo4j, enabling hybrid search with RAG and Cypher queries via a Streamlit dashboard
- Deployed the API on AWS Lightsail container services with CI/CD automation via GitHub Actions, ensuring stability through pytest unit and integration tests
- Designed and developed a comprehensive online course on data analysis using ChatGPT for professionals and learners, creating instructional videos and interactive Jupyter notebooks
- Utilized OBS and professional audio equipment to ensure high-quality video and audio content
- Led a CRM data normalization and cleaning project visualized via a Sankey diagram to aid customer understanding and pipeline development
- Implemented and validated a genAI-driven web crawling strategy on AWS, ensuring data quality, scalability, and CRM data augmentation
Viola H.
Last position:
Content & Networking Strategist at We Are SPARKS
- Workshops & masterclasses on strategic networking
- Advising C-level clients on visibility and building a high-profile network both on and beyond LinkedIn
Mobin R.
Last position:
Senior Consultant and Chief Product Owner at Luxota Travel Tech
- Coached teams in Agile methodologies and Scrum framework implementation.
- Aligned the product roadmap with business goals such as increasing bookings, improving user experience, and expanding supplier integrations.
- Collaborated with developers and third-party providers to ensure smooth API integration and end-to-end functionality.
- Created and continuously refined the product backlog with user stories, bugs, and technical improvements.
- Prioritized backlog items based on business value, revenue impact, and regulatory deadlines.
- Worked closely with backend and frontend teams to clarify dependencies and prioritize technical debt.
Helmut B.
Last position:
Service Provider for Writers and Self-Publishers at Selfpublisher-Verband
- Offered proofreading services and creation of supplementary texts
- Provided book design and typesetting
- Coached authors in conducting readings and presentations
- Developed e-learning offerings
- Managed the blog “FragDenWortSupport”
Discover over 15,000 top freelancers
Statistics of experts using Large Language Model
Aggregated from the professional profiles of matched freelancers.
Experience
17 years (Germany: 15 years)
Position duration
3.8 years (Germany: 2.9 years)
Positions per freelancer
11 (Germany: 10)
Top business areas
Information Technology, Product Development, Project Management
Top industries
Information Technology, Banking and Finance, Professional Services
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
96% (Germany: 95%)
Master's degree or higher
58% (Germany: 70%)
Doctorate
17% (Germany: 13%)
Certifications per freelancer
3
Most common languages
German, English, French
Speak two or more languages
100% (Germany: 97%)
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 Frankfurt 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 Frankfurt 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 29 Oct 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 (86%)
- Banking and Finance (50%)
- Professional Services (46%)
- Education (39%)
- Manufacturing (36%)
- Healthcare (29%)
- Telecommunication (29%)
- Automotive (25%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Large Language Models do
A Large Language Model, or LLM, learns patterns in extensive text and code to generate, classify, summarize and transform language. Companies use LLMs for conversational assistants, document processing, search, content workflows and software support. Results depend on the model, the context provided and the controls around it.
Where LLMs create value
LLM projects often connect language understanding with existing business systems. Typical deliverables include:
- Customer and employee assistants grounded in approved content
- Retrieval-augmented generation for search and question answering
- Document extraction, classification and summarization pipelines
- Prompt, evaluation and guardrail workflows for production use
- Code and data interfaces that turn natural language into actions
Ecosystem and tooling
Specialists work across hosted models such as GPT and Claude, open-source models from ecosystems such as Hugging Face, and deployment options in major cloud environments. They may use embedding models, vector databases, orchestration libraries, model gateways and observability tools. Strong delivery also requires API integration, data preparation, Python or TypeScript, testing and secure access design.
When companies need expertise
Freelance expertise is useful when a proof of concept must become a dependable product, or when internal teams lack experience with model selection and evaluation. Companies also bring in specialists to reduce hallucinations, connect private data safely, improve response quality and control inference costs. In Frankfurt, teams can combine remote delivery with on-site workshops when product, legal and domain stakeholders need close coordination.
What strong specialists deliver
Good professionals begin with the user task and the available data rather than choosing a model first. They define measurable evaluation criteria, compare model behaviour on representative examples and make failure cases visible. Their work includes clear prompts, structured outputs, fallback paths, access controls, logging and human review where automated answers could create risk.
Skills beyond prompting
Prompt writing alone is not enough for a production LLM system. Look for specialists who understand retrieval, chunking, embeddings, fine-tuning decisions, context limits and inference trade-offs. They should also be comfortable with backend APIs, data privacy, threat modelling, monitoring and product discovery. The best fit can explain model limitations clearly to both technical and business teams.
Frequently asked questions
Need clarity? These are the questions we hear most often about Large Language Model.
A Large Language Model can power support assistants, internal search, document review, summarization, translation and natural-language interfaces to business software. It can also help professionals draft content or code, provided the workflow includes suitable validation and access controls.
An LLM handles varied language and can produce useful outputs when requests do not follow a fixed format. Traditional automation is usually more predictable for clearly structured rules, so many effective systems combine both approaches and reserve language models for interpretation or generation.
A Large Language Model should be selected according to quality, latency, data handling, integration needs and operational control. GPT and Claude can offer convenient hosted access, while an open-source model may suit teams that need more deployment or customization options.
A strong Large Language Model specialist often combines Python or TypeScript with API integration, retrieval-augmented generation, vector databases and evaluation methods. Experience with cloud deployment, security, data engineering and product discovery is valuable when the system must run reliably in production.
A Large Language Model proof of concept can be small, but production work needs experience with data quality, prompt versioning, evaluation, monitoring and failure handling. The right specialist should have solved problems similar to yours and be able to show how they tested reliability rather than only demonstrating a polished example.
A Large Language Model project is often well suited to remote collaboration because code, model access and evaluation data can be shared securely online. Frankfurt-based teams may still prefer on-site workshops for discovery, stakeholder alignment or sensitive process design, with day-to-day delivery handled remotely.
Evaluate a Large Language Model system with representative tasks, documented acceptance criteria and tests for factuality, relevance, refusal behaviour and data leakage. Ask the specialist to explain failure cases, monitoring and fallback behaviour instead of judging quality from a few impressive conversations.
A Large Language Model freelancer should clarify the target users, source data, permitted actions, security boundaries and definition of a useful answer. They should also establish who owns evaluation, how feedback is collected and which parts of the workflow require human approval.
The average hourly rate of freelancers in Frankfurt, Germany who have used Large Language Model in their recent projects is 100 €, which corresponds to a daily rate of about 800 € based on an 8-hour working day.
Of the freelancers in Frankfurt, Germany who have used Large Language Model in their recent projects, 96% hold at least a Bachelor's degree, 58% hold at least a Master's degree, and 17% hold a doctorate.
On average, freelancers in Frankfurt, Germany who have used Large Language Model in their recent projects have 17 years of professional experience, with a single engagement typically lasting around 3.8 years.
The most common languages among freelancers in Frankfurt, Germany who have used Large Language Model in their recent projects are German (100%), English (100%), and French (21%).
The most common industries among freelancers in Frankfurt, Germany who have used Large Language Model in their recent projects are Information Technology (86%), Banking and Finance (50%), and Professional Services (46%).
The most common business areas among freelancers in Frankfurt, Germany who have used Large Language Model in their recent projects are Information Technology (89%), Product Development (89%), and Project Management (57%).
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.
Berlin 
Hamburg 
Munich 
Cologne 
Stuttgart 
Dusseldorf 
Leipzig 
Dortmund 
Essen 
Bremen 
Nuremberg Request a free demo
Get in touch with the FRATCH team and we will get back to you within 4 hours.
Would you rather directly get in touch?
We always have the time for a call or email!
Philipp Thomaschewski
FRATCH CEO
