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Large Language Model Experts

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Hire experts who design LLM applications, connect GPT, Claude or Gemini models to business data, and build reliable retrieval, automation and evaluation workflows. FRATCH matches you quickly with vetted, available freelancers whose skills fit your project.

Meet FRATCH Experts who have recently used Large Language Model

Verified expert

Jens L.

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Interim Product Manager , Product Lead | B2B Platforms | AI Integration

Hamburg
Jens L.

Last position:

Consulting Digital Development & Transformation at European Mar GmbH

Developed the product vision and outcome strategy for a new registry platform, designed a management workshop for strategic alignment, conducted BPMN process mapping across systems and roles, analyzed user story practices and further developed them on an outcome basis, and brought together stakeholders from business departments, management and the external development partner.

Skills: Product Strategy, Outcome Roadmapping, Stakeholder Workshops, BPMN/Process Analysis, Miro, Linear

Verified expert

Fadi S.

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Operational AI & Data Engineering Consultant

Oberhausen
Fadi S.

Last position:

AI-Engineer at Recruitment Agency

  • Design, development, and productive rollout of an Enterprise HR Copilot based on Microsoft Copilot Studio to automate internal HR processes
  • Development of an AI-supported assistant for answering employee inquiries based on a SharePoint knowledge base and internal company HR documentation
  • Integration of the Copilot into Microsoft Teams, including welcome logic, user guidance, and role-based routing
  • Automation of HR processes and approval workflows with Microsoft Power Automate to reduce manual processing steps
  • Development of realistic HR documents and response templates with Azure OpenAI, taking business requirements and consistent company policies into account
  • Implementation of prompt strategies, knowledge management, and response optimization to improve response quality and user acceptance
  • Close collaboration with business departments to analyze requirements and integrate the AI solution into existing company processes

Technologies:

Microsoft Copilot Studio, Microsoft Teams, Azure OpenAI, SharePoint, Power Automate, Power Platform, Microsoft 365, Prompt Engineering, Workflow Automation

Verified expert

William N.

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Senior/Lead Business Analyst & AI Workflow Consultant | Requirements Engineering | BI | Workflow Automation | Claude Code

Berlin
William N.

Last position:

Power BI Solutions Architect/Engineer & AI Consultant at AVERDUNG GmbH

  • Redesign of the company's BI infrastructure: replacement of a fragmented landscape of manually maintained Excel solutions and CSV imports with a centralized Power BI environment featuring a unified data model as the company-wide single source of truth
  • Consolidation of previously isolated reporting logic into a central semantic model – eliminating redundant files, manual data transfers, and inconsistent metrics between departments
  • Forecasting & planning: Design and implementation of company-wide liquidity planning in Power BI – from business logic to a fully automated, data-source-driven planning model replacing the previous manual Excel process; enables rolling forecasts and continuously up-to-date cash flow transparency for management
  • Optimization of existing Power BI dashboards in terms of performance, structure, and analytical value using an AI-native approach
  • Analysis and improvement of the data model, including data quality analyses, data cleansing, and consistent modeling using star schema, DAX, and Power Query
  • Incident & anomaly analysis: Identification, investigation, and explanation of data anomalies, including root-cause analysis and concrete recommendations for action
  • AI solution architecture: Connecting Business Central and Power BI to LangDock via MCP (Model Context Protocol) for AI-supported data usage
  • Creation of a historical data layer as a basis for trend and time-series analyses
  • AI-supported automation: Design and development of AI skills, agents, loops, and processes for the automated analysis and interpretation of reports
  • Automated reporting workflow: Setup of scheduled, automated email distribution of AI-generated analyses and recommendations to stakeholders
  • Gathering and documentation of business requirements and coordination with business departments and IT as part of requirements engineering / product owner activities
  • Breaking down overall requirements into clearly defined work packages and tasks
  • Definition, prioritization, and management of milestones throughout the entire project lifecycle

Tools: POWER BI, M365, Copilot Studio, MIRO, Microsoft Business Central, Microsoft Fabric, Claude AI, ChatGPT, LangDock, MS VS Code

Verified expert

Patrick L.

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Senior AI Software Engineer with 9 years of experience delivering practical AI products for enterprise and public sector

Frankfurt am Main
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

Verified expert

Kareem S.

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Business Lawyer | Business Analyst & Requirements Engineer | GenAI & Agile IT Transformation (PSPO I)

Alzenau
Kareem S.

Last position:

Business Analyst & Consulting Manager at S&M Unternehmensberatung PartG

  • I support medium-sized clients with their funding needs.
  • I evaluate operational business processes and translate complex legal and regulatory requirements into business requirements, target models and actionable roadmap epics.
  • I use Generative AI tools and automation in a targeted way to efficiently create requirements documentation, process analyses, evaluations, meeting preparation materials, customer analyses and decision papers.
Verified expert

Qamar H.

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Freelancer

Runkel
Qamar H.

Last position:

Freelance Consultant Data Analytics & AI Portfolio at TIC Company

  • Support for a data, analytics and AI initiative in a regulated enterprise environment by structuring, evaluating and prioritizing several data-driven use cases based on business impact, feasibility, scalability, data maturity and governance requirements.
  • Translation of complex business and analytics requirements into clear product, data and implementation logic, as well as preparation of decision-ready documents, target visions and roadmap inputs for stakeholder and management discussions.
Verified expert

Gabin Maxime N.

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AI/ML Engineer · Agentic AI

Freising
Gabin Maxime N.

Last position:

Multi-Agent R&D Pipeline (3 Custom Agents) at Independent Project

  • Claude Code subagents, MCP, Pydantic V2, pytest, bandit

  • Designed and shipped 3 specialized agents that hand work down a line: a research agent writes a cited implementation spec, a coding agent builds the modular code and its tests, a review agent ranks findings by severity and applies the fixes. Each handoff is a structured document, so no stage depends on another agent's context window.

  • Connected the research agent to an academic-research MCP server (Semantic Scholar, ArXiv, Hugging Face Hub, citation snowballing) so every reference traces to a tool result rather than the model. Gated commits behind ruff, mypy, pytest and bandit, required human sign-off before installs and commits, and persisted session state on disk so long runs survive a context reset.

Verified expert

Alexander H.

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Senior Product & Strategy Consultant Strategic Product Management, Product & Platform Development, Go-to-Market,

Frankfurt am Main
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

Verified expert

Onur K.

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AI & Automation Consultant · Project Manager for AI Projects

Braunschweig
Onur K.

Last position:

Project Manager & Outsourcing Manager at SENEC GmbH (EnBW Group)

  • Built a scalable nearshore IT developer hub (Croatia, Czech Republic, Poland) as an independent company through a BOT model (Build – Operate – Transfer)
  • Identified, selected, and managed full-service agencies; introduced management and control mechanisms, including KPIs, SLAs, and regular service reviews
  • Prepared and reviewed data processing agreements and framework contracts in coordination with Legal & Compliance; integrated regulatory requirements (including KRITIS) into process design
  • Advised on cloud vs. on-premise strategies, data storage, and authorization concepts; supported Procurement with tendering and service provider evaluations
  • Managed change and process harmonization between internal teams and nearshore partners; reported to executive management, CFO, and CIO

Result: Scalable IT developer hub with an audit-ready governance model, reduced operating costs, and accelerated product development.

Verified expert

Ornel Franck W.

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Purchasing Manager, Logistics, IT Manager & Software Architect

Frankfurt am Main
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
Verified expert

Thomas P.

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Project Manager, Product Owner & AI Consultant, AI Transformation - ERP · Shop · Marketing

Leipzig
Thomas P.

Last position:

Product Owner & AI Automation Architect (B2B) at Ihre-Hygieneberatung

  • Development of a digital audit application for inspections in medical facilities. The goal is to connect on-site data collection, voice recording, documentation and downstream processes in one end-to-end, AI-supported workflow.

  • Design and development of a Flutter audit app with Claude Code for the structured execution and documentation of inspections.

  • Processing of voice recordings captured in the app through automatic transcription and AI-supported creation of structured inspection reports, followed by an approval process

  • Connecting various data sources such as email, Odoo 19, attendance records and Google Drive via n8n to automate billing and follow-up processes

  • Automatic provision of required documents and email delivery through n8n-controlled workflows, including the use of LLMs for text creation

  • Skills: Flutter, Claude Code, LLM Integration, n8n, Odoo 19, Google Drive, Process Automation

Verified expert

Stefan O.

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AI Product Leader

Berlin
Stefan O.

Last position:

Founder at ProtocolEngine.io

Evidence-led health intelligence platform turning published research into personal health protocols. It scores 430 habits, foods, and supplements against the studies behind them, and moves the score when the evidence moves. Built solo.

  • Built the daily ingestion pipeline across PubMed, bioRxiv, and medRxiv: 43,000+ papers from 3,400+ journals processed into 230,000+ typed evidence claims, each one traceable back to the study it came from.
  • Designed the six-factor evidence scoring model and the public changelog behind it, so no recommendation ever appears without the papers underneath it. 23,000+ grade changes recorded and explained to date.
  • Shipped an entity information model connecting every intervention to its mechanisms, biomarkers, and outcomes: 118 biomarkers with region-specific reference ranges, 77 mechanisms, 32 graded outcomes.
  • Built the personalisation layer: blood panel ingestion that reads lab PDFs with a vision model and corrects results for draw time against the user's wake anchor, plus Oura, WHOOP, and Withings integration for daily readiness context.
  • Operate eleven specialised review agents over the corpus and codebase, covering paper curation, retrieval quality, health-claim compliance across EU and US regimes, and security.
  • Shipped the Evidence Assistant, a RAG assistant that answers from the claim database and cites the underlying papers, plus a B2B practitioner tier, an Expo React Native app, and localisation across 3 languages and 7 markets.

Stack: Next.js 16, TypeScript, Supabase, pgvector, Anthropic Claude, Vercel, DeepInfra.

Verified expert

Sascha B.

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Frontend / UX Engineer

Berlin
Sascha B.

Last position:

Web Developer at GxPlex

  • Built a customized MediaWiki instance, including installation, MySQL database, SSL, and automatic backups
  • Set up user roles (Admin, Mod, Verified, User) and a permissions system
  • FlaggedRevisions for editorial review workflows · Commenting and rating extensions
Verified expert

Jens R.

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Technical Product Owner

Kerpen
Jens R.

Last position:

Platform Architect & Senior Developer at Direct client, industrial measurement technology, medium-sized company

  • Technical leadership across hardware, firmware, and software teams; scope: hardware/firmware team (4 people) and leadership group (5 people)
  • Consolidated and documented a product family that had grown over more than 15 years and aligned it with CRA compliance — from the bare-metal I/O module to the cloud interface.
  • Provided the most important customer product with the essential requirements and architecture documentation within two months — for a firmware landscape that had grown over more than 15 years. It now supports the customer’s modernization strategy.
  • Established a monthly reporting line to the supervisory board and executive board within three months: nine meetings since 12/2025. The report itself is versioned and built from the CI pipeline; it is based on automatically collected activity and release data instead of assessments.
  • Built a container-based CI/CD infrastructure from scratch: cross-compilation, host tests, and documentation builds in one continuous pipeline.
  • Introduced declarative QA gates for DevOps and development artifacts — from the start using lefthook instead of pre-commit, executed in a dedicated container image.

Technologies used: arc42, req42, tpo42, docToolchain, PlantUML, ArchiMate, C4 model, ADR, C, C++ (GTest), CMake, Bare Metal (ARM Cortex-M3/M7), OCI containers, Jenkins, lefthook, Prometheus, Grafana, SBOM, CRA, OPC, SCADA, PLC integration, IPv6 migration, Zero Trust, Sociocracy 3.0, Cynefin

Discover over 15,000 top freelancers

Statistics of experts using Large Language Model

Aggregated from the professional profiles of matched freelancers.

Experience

15 years

Large Language Model experts have 15 years of professional experience on average.

Position duration

2.9 years

Large Language Model experts stay in a single position for 2.9 years on average.

Positions per freelancer

10

Large Language Model experts have completed 10 positions on average over the course of their careers.

Top business areas

Information Technology, Product Development, Research and Development

Large Language Model experts have gathered most of their hands-on project experience in Information Technology, Product Development, and Research and Development.

Top industries

Information Technology, Professional Services, Education

Large Language Model experts are most in demand in Information Technology, Professional Services, and Education.

Certification focus areas

Information Technology, Project Management, Product Development

Large Language Model experts earn their certifications most often in Information Technology, Project Management, and Product Development.

Bachelor's degree or higher

95%

95% of Large Language Model experts hold at least a Bachelor's degree.

Master's degree or higher

70%

70% of Large Language Model experts hold at least a Master's degree.

Doctorate

13%

13% of Large Language Model experts have a doctorate (PhD).

Certifications per freelancer

3

Large Language Model experts hold 3 professional certifications on average.

Most common languages

German, English, French

Large Language Model experts most often speak German, English, and French.

Speak two or more languages

97%

97% of Large Language Model experts speak two or more languages.

Based on our profile pool as of 16 Oct 2026.

Daily rate distribution

0% 25% 50% 75% 100%
9% of Large Language Model experts charge less than €400 per day.
40% of Large Language Model experts charge between €400 and €800 per day.
40% of Large Language Model experts charge between €800 and €1200 per day.
7% of Large Language Model experts charge between €1200 and €1600 per day.
4% of Large Language Model experts charge €1600 or more per day.
<€400 €400-​800 €800-​1200 €1200-​1600 €1600+

The chart shows how the daily rates of experts in this technology 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 using Large Language Model

Rates are based on recent contracts and do not include FRATCH margin.

1000
750
500
250
Rate comparison chart
Daily rate avg. 767 €

The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.

1000
750
500
250
Rate comparison chart
Median rate 800 €

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 16 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 (91%)
  • Professional Services (43%)
  • Education (39%)
  • Banking and Finance (37%)
  • Manufacturing (37%)
  • Automotive (36%)
  • Retail (32%)
  • Healthcare (30%)

Please note that freelancers can work across multiple industries, so percentages overlap.

About the technology

What LLMs do

A Large Language Model, or LLM, processes and generates natural language from patterns learned across large datasets. Companies use models such as GPT, Claude and Gemini to power conversational products, document workflows, search assistants, content tools and software interfaces. The model is only one part of a production system: data, prompts, evaluation, security and user experience determine its value.

Common applications

LLM solutions appear in customer support, internal knowledge search, sales operations, legal review, research and software tooling. Typical deliverables include:

  • Retrieval-augmented generation using company documents
  • Structured extraction from emails, contracts and reports
  • Conversational interfaces with tool and API access
  • Classification, summarization and multilingual workflows
  • Guardrails, monitoring and human review paths

Ecosystem and tooling

Strong work with LLMs spans model APIs, open-weight models and cloud services. Specialists may use OpenAI, Anthropic, Google Vertex AI, Hugging Face, Azure AI or Amazon Bedrock, alongside vector databases such as Pinecone, Weaviate, pgvector and Elasticsearch. Python, TypeScript, orchestration libraries, embedding pipelines and observability tools often connect these pieces into a maintainable application.

When companies need expertise

Freelance support is useful when a proof of concept must become a dependable product, or when an existing chatbot gives inconsistent answers. Companies also bring in specialists to select a model, prepare private data, reduce latency, control usage costs and establish evaluation methods. Expertise matters most when sensitive information, complex integrations or demanding response quality are involved.

Skills that set experts apart

Good LLM professionals understand both model behavior and the surrounding software system. They distinguish prompt changes from changes that require retrieval, fine-tuning or a different model. They test factuality, relevance, refusal behavior and injection resistance with representative cases rather than relying on impressive demos. Clear documentation, versioned prompts and measurable acceptance criteria make their work easier to operate.

Delivery and collaboration

An effective engagement starts with concrete user journeys, available data and clear failure conditions. The specialist should define an evaluation set, select an appropriate model strategy and deliver a small vertical slice before expanding scope. Remote collaboration works well when access, review routines and data handling are agreed early; on-site work can help when teams need close workshops around proprietary processes.

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Frequently asked questions

Key details about Large Language Model, drawn from the questions we get asked most.

Companies use Large Language Models for conversational support, document search, summarization, classification, content assistance and workflow automation. They can also connect business systems to natural-language interfaces, provided the application includes suitable permissions, validation and monitoring.

An LLM application can interpret varied language and generate responses instead of relying only on fixed rules or scripted paths. A strong solution still needs defined intents, retrieval or tool access, safeguards and tests, so it is not simply a model connected to a chat screen.

The right GPT alternative depends on task quality, context handling, data controls, hosting requirements, latency and integration needs. A specialist can compare GPT, Claude, Gemini and open-weight models against representative company cases instead of choosing from general benchmarks alone.

An LLM specialist should usually understand APIs, Python or TypeScript, data preparation, embeddings, vector search and cloud deployment. Experience with security, evaluation, observability and user experience is equally important for production work.

The required Large Language Model experience depends on risk and scope, not on a fixed project duration. A simple internal prototype may need focused model integration, while regulated workflows or customer-facing automation require proven evaluation, privacy controls and reliable system design.

Most LLM work can be delivered remotely through secure repositories, controlled data access and structured reviews. On-site sessions can help when specialists must map proprietary processes, interview subject experts or align several teams on acceptable model behavior.

Ask an LLM professional to explain how they measure factuality, relevance, latency, refusal behavior and resistance to prompt injection. Review a small evaluation plan, sample failure analysis and deployment approach rather than judging quality from a polished demo alone.

For Large Language Models, retrieval is often better when the system must use changing private information, while prompt design can improve instructions and output structure. Fine-tuning may help with consistent style, classification or specialized behavior, but it does not replace current source data or application safeguards.

The average hourly rate of freelancers who have used Large Language Model in their recent projects is 96 €, which corresponds to a daily rate of about 767 € based on an 8-hour working day.

Of the freelancers who have used Large Language Model in their recent projects, 95% hold at least a Bachelor's degree, 70% hold at least a Master's degree, and 13% hold a doctorate.

On average, freelancers who have used Large Language Model in their recent projects have 15 years of professional experience, with a single engagement typically lasting around 2.9 years.

The most common languages among freelancers who have used Large Language Model in their recent projects are German (97%), English (97%), and French (18%).

The most common industries among freelancers who have used Large Language Model in their recent projects are Information Technology (91%), Professional Services (43%), and Education (39%).

The most common business areas among freelancers who have used Large Language Model in their recent projects are Information Technology (94%), Product Development (87%), and Research and Development (54%).

Main locations of FRATCH Experts, who have recently used Large Language Model

Our freelancers and interim experts are at home all over Germany — available on-site in Berlin, Hamburg, Munich and every major business hub, or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.

In Austria our freelancers and interim experts support companies from Vienna to Graz — on-site where your project needs them, or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.

Across Switzerland our specialists are active in Zurich, Geneva, Basel and Bern — working on-site or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.

Zurich Geneva Basel Bern

Countries:

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