Large Language Model Experts in Hamburg
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Meet FRATCH Experts in Hamburg, who have recently used Large Language Model
Thomas Kostrewa
Last position:
Agile Coach / Release Train Engineer (SAFe) – Product & Cross-functional Delivery Focus at Autonomous Driving / Connectivity (OEM, confidential)
- Orchestrate cross-functional delivery across organisational units in the Connectivity domain, aligning teams around integrated end-to-end, customer-testable value rather than isolated component delivery.
- Drive a shift from local component optimisation towards shared outcomes and a common delivery goal, increasing focus and enabling significantly faster integrated delivery.
- Coordinate across 15 cross-functional organisations in a highly complex OEM environment; bring Product, Engineering, Programme Management and specialist functions together to resolve dependencies and improve decision-making.
- Coach Product Managers, Product Owners and stakeholders on product responsibility, prioritisation, outcome orientation and aligned backlogs.
- Use Claude through an AWS Bedrock integration to analyse Jira and Confluence content, identify patterns, dependencies and quality gaps, and support structured product and delivery decisions.
- Establish AI-native requirements excellence with LLM-supported quality gates for epics, features, stories, acceptance criteria, roadmaps and task breakdowns; scale adoption through templates and prompt playbooks.
Hoa Josef Nguyen
Last position:
AI Architect and Enabler at Inhouse / AI Business
Technologies: n8n, Notion, OpenAI API, Claude, MS AI Foundry, MS CoPilot Studio, MS CoPilot, LLM, Node.js, Vercel, LangGraph, PostgreSQL, pgEdge, pgvector, Docker, LangChain, Ollama, Open WebUI
- Continuous evaluation and prioritization of internal automation needs
- ~20 AI agents in active use: research, content pipelines, document processing
- 5 n8n workflows for automated data and process control
- Architecture built on the same principles as in customer projects: state management, event-driven orchestration, API integration
- Ongoing operation and further development
Sanchit Bhavsar
Last position:
Freelancer at S2S Dynamics UG
- Implementing cross-industry applications with LLMs
- Developing cloud infrastructure for clients
- Implemented end-to-end data pipeline to deploy models in real time
- Managed overall IT system administration and desktop support
Anastasiia Komarenko
Last position:
Senior Test Automation Engineer at E.ON
- Reviewing functional and technical requirements from a testing perspective
- Creating test cases and automated tests to validate requirements
- Performing manual and automated functional, end-to-end, and regression tests
- Documenting test results and tracking defects
- Using models like GPT-4, BERT, and Hugging Face Transformers for automated test case generation, analysis of test results, and improving test coverage, including bias checks and security reviews
- Techs: MS Office, Jira, Zephyr, Confluence, Tosca, stakeholder communication, Agile, Kanban, Scrum, OpenAI API, Hugging Face, PyTorch, LangChain.
Dieter Ratz
Last position:
Driver analyses at Genactis GmbH
- Calculation of attribute importance based on driver analyses
- Interpretation, reporting, and consulting
Heena Patel
Last position:
Retirement Spend & Tax Optimizer Agentic AI App (Vibe Coding) at Personal Project
Self-directed exploration of agentic AI development methods, taken from idea to a working, publicly usable application
- Built an interactive planning tool for modelling retirement withdrawals and tax strategy using an agentic AI (vibe coding) development approach – demonstrating self-directed investigation of new AI-assisted development methods
- Delivered live, tax-aware spending projections and adjustable user inputs; shipped as a free, install-free browser application built in Python, with attention to usability for non-technical users
Bogdan Melnychuk
Last position:
Tech Lead at cirplus
- Sole technology owner, leading architecture, development, operations, and infrastructure. Leveraging AI to accelerate work in areas like front-end and design.
- Built full-stack solutions (backend, React front-end) with CI/CD pipelines and observability standards, setting the foundation for an engineering organization.
- Delivered AI-driven features using LLMs, automating supplier–buyer matching, lead generation, email campaigns, and reducing manual effort.
- Reduced cloud costs by 90% by migrating the system to an AWS serverless architecture
Dominic Dreiner
Last position:
Senior GEO Strategist & AI Search Consultant at Venmate GmbH
- Industry: SaaS (Customer Success Management) – B2B
Comprehensive GEO implementation (Generative Engine Optimization) for a B2B CSM SaaS startup with a focus on AI Search Visibility and AI Citations in ChatGPT, Perplexity and Google AI Overviews – including a GEO workshop and a prioritized implementation roadmap.
- GEO Workshop & Roadmap: Conceptualized and delivered a GEO workshop (how LLMs work, AI Search Architecture, citation strategies); translated it into a prioritized implementation roadmap with impact/effort estimates
- Technical GEO Foundation: Robots.txt & llms.txt, Core Web Vitals & page speed, XML sitemaps for AI crawlers
- Schema & Structured Data: Gap analysis, implementation of Product/Service, FAQ, Article, Author and Organization schema, validation of coverage
- Content Architecture & GEO: Flat content architecture, topic clusters (pillar-and-spoke), 200–300 word chunks and answer-first content structure, E-E-A-T rollout through author pages
- Query Fan-Out & Prompt Anticipation: User prompt mapping, query fan-out, content gap analysis based on prompt coverage
- Citation & Backlink Strategy: AI citation analysis (news, Reddit, Wikipedia, listicles) and development of outreach and backlink strategies
Tools & technologies: Otterly.AI, PeecAI, Qforia (iPullRank), Schema Markup Validator, OpenAI Tokenizer, Prompt Coverage Monitor, ChatGPT, Perplexity AI, Semrush, Ahrefs, Framer CMS
Thomas Wittlinger
Last position:
Chief Product Officer at OWNLY FinTech GmbH
Freelance work for a large German family office
Built and further developed a modular B2B SaaS platform for professional wealth management and family offices, alongside freelance delivery of production-ready AI, data management, and automation solutions for the wealth management sector.
- Developed an AI governance framework for regulated finance and asset management workflows, aligned with DORA, BaFin-related governance expectations, and data protection requirements, including role definitions, access levels, and decision rules.
- Designed an agentic system with a locally operated open-source language model, including Qwen2.5 via Ollama, for secure querying of an asset database through text-to-SQL-to-text workflows.
- Built AI-supported analysis and reporting capabilities that generate structured answers, tables, and charts from asset data, with domain validation through resolver logic and RAG elements.
- Developed production-grade data import workflows for financial service provider data from CSV, PDF, and API sources, including validation, plausibility checks, and reconciliation with existing asset data.
- Solved the asset matching problem without a cross-system primary key through multi-stage validation rules and human-in-the-loop approvals.
Results:
- Secured EUR 250,000 in SaaS revenue in 2024, exceeding the forecast by 20%.
- Acquired family office clients with EUR 1.6bn in assets under management.
- Reduced manual effort for the largest client by approx. 3 days per month through automated data import and reconciliation processes.
- Reduced operational error risk through structured data validation, multi-stage asset matching, and human-in-the-loop approvals.
Marcus Brandt
Last position:
Managing Director at Petermann Brandt GmbH
- Development and implementation of custom IT solutions for key customers.
- More than 15 years of experience in IT and project management, disciplinary leadership of up to 80 employees.
Tungi Dang
Last position:
Technical PMO | Delivery Master | LLM-Expert at Stealth - NDA
- Owning RAG, LLM-System, ML-ops-Pipelines for various startups in Insurance, Banking, Energy (KRITIS)
Maryam Mouzarani
Last position:
AI Red Team Engineer at Applause
- Performed security assessments and penetration testing on Microsoft AI models for text, image, and video generation.
- Conducted prompt injection attacks through diverse input vectors, including crafted text, steganographic images, and manipulated visual elements (e.g., varying opacity and embedded content).
John Von Saurma
Last position:
Interim Head of Content & Social Media at Luckychef.com
- Creation and planning of the content plan
- Editorial planning for 10 channels
- Image shoot (organization, coordination, execution)
- Selection of image, text and video content
Basar Deniz
Last position:
Product & Growth Advisor at Votmode Handcraft
- Led 0→1 launch of a small D2C product, including website and CRM flows.
- Used AI and no-code tools to rapidly prototype, iterate, and operate with minimal overhead.
- Owned end-to-end product decisions across UX, content, analytics, and operations.
Jan Hoppe
Last position:
Founder & Chief Product Officer at ganzheit
- Founded a bootstrapped AI-native startup building digital tools for psychotherapists
- Empowered therapists to enhance therapeutic work without disrupting human connection
- Assembled founding team and reached product-market fit with LLM-powered supervision for psychotherapists
- Integrated generative AI stack, including agentic coding, evaluations, tracing, and prompt management
Discover over 15,000 top freelancers
Statistics of experts using Large Language Model
Aggregated from the professional profiles of matched freelancers.
Experience
15 years
Position duration
2.2 years (Germany: 2.9 years)
Positions per freelancer
10 (Germany: 9)
Top business areas
Information Technology, Product Development, Project Management
Top industries
Information Technology, Professional Services, Banking and Finance
Certification focus areas
Information Technology, Project Management, Business Intelligence
Bachelor's degree or higher
100% (Germany: 96%)
Master's degree or higher
68% (Germany: 72%)
Doctorate
24% (Germany: 14%)
Certifications per freelancer
2 (Germany: 3)
Most common languages
German, English, French
Speak two or more languages
96% (Germany: 97%)
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 Hamburg 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 Hamburg 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 covers
A large language model, often called an LLM, is a system trained to work with text and other language-heavy inputs. Companies use it for chat assistants, document search, content support, and workflow automation. The work usually sits inside products, knowledge systems, or internal tools.
Typical work
- Prompt design and response behavior
- RAG workflows with company documents
- GPT-based features and API integration
- Output filtering, safety, and evaluation
- Support for German and English use cases
Ecosystem
Strong specialists know the model APIs, embeddings, vector stores, and orchestration layers that surround an LLM setup. They also work with common stacks for retrieval, logging, monitoring, and testing. Good work depends on clean data access and clear rules for what the model may answer.
When to bring in help
Companies usually bring in freelance expertise when they want to prototype fast, improve an existing chatbot, or connect an LLM to private content. It also helps when a team needs clearer prompts, lower hallucination risk, or a better way to judge output quality. In Hamburg, this often matters for media, logistics, commerce, and enterprise teams with both German and English content.
What strong specialists do
A strong professional does more than call an API. They shape prompts, choose retrieval methods, test edge cases, and tune the setup for accuracy, latency, and cost. They document the limits clearly so product, legal, and support teams know how the system behaves.
Skills that matter
- Prompt engineering and evaluation
- Retrieval-augmented generation design
- API, backend, and data integration
- Structured output and guardrails
- Clear communication with technical and non-technical teams
Frequently asked questions
What clients ask us most about Large Language Model — answered in short.
A Large Language Model is used for text-heavy work such as support assistants, document search, summarization, drafting, and internal knowledge tools. In many projects it sits behind a product feature rather than as a visible app on its own. Companies often ask specialists to make it reliable, grounded in their own data, and safe to use.
No, but they are closely related. LLM is the broader term for the technology class, while GPT refers to a family of well-known models from OpenAI. A good specialist can work with GPT-based systems or other model families depending on the use case.
A strong Large Language Model specialist usually also knows retrieval, embeddings, vector search, API integration, and evaluation methods. Experience with backend systems, data access, and output validation matters just as much as prompt writing. For many projects, that mix is what makes the result usable in production.
If the use case is simple, even a short discovery phase can help avoid bad design choices. For anything that touches customer service, internal knowledge, or regulated content, companies usually want someone who has shipped LLM features before. The more sensitive the output, the more important proven judgment becomes.
Both can work well. Large Language Model work is often remote-friendly because most tasks happen in code, prompts, and evaluation sets, but on-site sessions can help when teams need to align on data, policy, or product goals. In Hamburg, hybrid work is common when local teams want close collaboration with specialists in German and English.
Look for clear examples of shipped systems, not just prompt samples. A good LLM professional can explain how they reduced hallucinations, handled fallback behavior, and tested answers against real user cases. Strong communication matters too, because the work usually spans product, engineering, and business teams.
Traditional NLP tools are usually built for narrower tasks like classification, extraction, or rule-based text handling. A Large Language Model can do many of those tasks in one system, but it also needs tighter control because it can generate free-form answers. The right choice depends on whether you need flexibility or strict predictability.
Common adjacent pieces include vector databases, embeddings, retrieval pipelines, observability, and content filters. A Large Language Model project may also need document parsing, permission-aware search, and feedback loops for quality checks. If the system must support German content in Hamburg, language handling and terminology quality become even more important.
The average hourly rate of freelancers in Hamburg, Germany who have used Large Language Model in their recent projects is 108 €, which corresponds to a daily rate of about 864 € based on an 8-hour working day.
Of the freelancers in Hamburg, Germany who have used Large Language Model in their recent projects, 100% hold at least a Bachelor's degree, 68% hold at least a Master's degree, and 24% hold a doctorate.
On average, freelancers in Hamburg, Germany who have used Large Language Model in their recent projects have 15 years of professional experience, with a single engagement typically lasting around 2.2 years.
The most common languages among freelancers in Hamburg, Germany who have used Large Language Model in their recent projects are German (96%), English (93%), and French (33%).
The most common industries among freelancers in Hamburg, Germany who have used Large Language Model in their recent projects are Information Technology (89%), Professional Services (56%), and Banking and Finance (44%).
The most common business areas among freelancers in Hamburg, Germany who have used Large Language Model in their recent projects are Information Technology (96%), Product Development (81%), and Project Management (63%).
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.
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Munich 
Cologne 
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Stuttgart 
Dusseldorf 
Dortmund 
Essen 
Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
