Large Language Model Experts
matched in minutes from over 15,000 CVsHire 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
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 using a unified data model as the company-wide single source of truth
- Consolidation of previously isolated reporting logic in a central semantic model – elimination of redundant files, manual data transfers and inconsistent metrics across 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; enabling 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 specific recommendations for action
- AI Solution Architecture: Connecting Business Central and Power BI to LangDock via MCP (Model Context Protocol) for AI-supported data usage
- Development 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 delivery 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
Lutz E.
Last position:
Freelance Software Developer – GoPro MultiCam Control via COHN at Plutos Company UG
- Developed a pilot system to control and monitor multiple GoPro cameras
- Implemented start, stop, status, media, download and diagnostic functions via COHN endpoints
- Built camera detection, individual and group control, and stable session/KeepAlive logic
- Set up a download pipeline with a queue, retry handling and temporary files
- Ensured consistent session, folder and file naming for multicamera recordings
- Integrated provisioning, certificates and network operations
Franz B.
Last position:
Product Development (AI) at Self-initiated
AI phone assistant platform
Claude Code, Google AI Studio, Python, LLM / Voice-AI, PostgreSQL
- Designed and hands-on developed an AI-powered phone assistant platform (Voice-AI / LLM)—from idea and architecture through to MVP/product.
- Built agentic workflows and end-to-end automations with Claude Code and Google AI Studio.
- Also supported AI-powered delivery in client engagements: using Claude Code for governance documentation, requirements drafts, and automations.
Shamaila M.
Last position:
Founder/Kubernetes and Cloud Architect at Kubekanvas
- Developed browser based platform for kubernetes no-code deployment and cluster management
- Developed CLI in Typescript to deploy the resources in the cluster without leaving the browser UI.
- Implemented DevSecOps pipelines: image scanning, SBOM, policy enforcement, supply-chain security and used Kyverno. Implemented IAM integration for the command line utility tool.
- Designed role & permission models for Keycloak, oAuth/OIDC, social login flows.
- Used LLM to convert user intent into the diagram.
- Worked on integration with multiple Sovereign Cloud like stackIt, Hetzner, CIVO, UpCloud plus public clouds like AWS, GCP, Azure
- The technology stack includes Java, SpringBoot, Kubernetes, OpenAI, Kubernetes multi-tenancy using vCluster, Karpenter, RBAC for CLI, Helm, React
Fadi S.
Last position:
Data Scientist at E-Commerce
- Designed and developed a production-ready data platform based on Microsoft Fabric for an e-commerce company
- Built a scalable Medallion Architecture (Bronze, Silver, Gold) with Lakehouse and Delta Lake as the foundation for analytics and AI applications
- Developed a metadata-driven ETL platform with PySpark and a generic Incremental Loading Framework (SQL Server → Bronze → Silver → Gold)
- Developed reusable components for Microsoft Fabric Notebooks to standardize data integration, transformation, and validation
- Implemented automated data pipelines including monitoring, logging, error handling, and Delta MERGE strategies for stable production operations
- Built a business layer architecture for reporting, data warehousing, and reusable data products
- Integrated heterogeneous data sources via SQL Server, REST APIs, Amazon SP API, and Google Analytics 4
- Worked closely with business departments to analyze business requirements and translate them into scalable data architectures
Technologies: Microsoft Fabric, PySpark, Python, SQL Server, Delta Lake, Lakehouse, OneLake, Data Factory, Data Warehouse, REST APIs, Amazon SP-API, Google Analytics 4
Vaios K.
Last position:
Product Manager & Strategic Advisor at Freelance, Lovable, Dreamarium
- Advise early-stage startups on product vision, roadmapping, and GTM strategy, combining PMP and Stanford PM training with hands-on technical execution.
- Lovable: built and led a 200+ member community program for AI-native founders, bridging real-time founder feedback into product roadmap decisions.
- Lead customer discovery and OKR design engagements, scoping AI integration opportunities across client products.
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
Christian F.
Last position:
Project Lead & Cyber Security Consultant at Deutsche Börse Gruppe
- Established a cyber resilience framework for AI-accelerated threats in the Frontier AI Threat Endurance (FATE) program: capability assessments, maturity assessments and gap analyses covering cyber security, operational resilience, incident response, recovery and governance, followed by target concepts and improvement measures
- Supported action owners from specification through implementation: implementation roadmaps, decision papers, management presentations and governance reporting, as well as audit- and supervision-ready documentation; managed stakeholders from IT, Information Security, Risk Management, Operational Resilience, Business Continuity and the business units
Karen M.
Last position:
Personal AI Engineering Project — Croky AI at Crocky AI
Product:
- Built a production-ready AI platform for generating brand-aware marketing images and videos from product data, user requirements, and uploaded media.
- Own the platform architecture, technical roadmap, API design, security, deployment workflow, operational reliability, and model-provider strategy.
- Developed the core platform in .NET and built supporting AI and workflow prototypes in Python, applying language-independent API contracts and structured interfaces between services and model providers.
- Implemented reliable background processing with RabbitMQ, persisted workflow state, idempotent handling, retries, failure recovery, logging, secure storage, authorization, and credit accounting.
- Made pragmatic build-versus-buy and model-routing decisions based on reliability, latency, cost, and maintainability rather than novelty.
Agent Orchestration & RAG Systems
- Built and compared agent workflows using Microsoft Agent Framework, LangGraph, and LangChain, including tool use, conditional routing, clarification steps, state management, and hand-offs between agents.
- Implemented reusable .NET components for agents, prompts, tools, model providers, structured responses, and retrieval with pyvector, making it easier to change AI providers without rewriting the core workflow.
Derek K.
Last position:
Founder at FiftyForward
Building a company to "support people over 50 as they start a new career"
Focus: "AI first" approaches to establish all relevant processes reliably in the shortest possible time
Core areas:
Business development (including competitor monitoring, channel selection, B2B and B2C strategies)
Marketing (building and optimizing funnels, organic and with paid ads)
Sales (direct B2B and B2C outreach resulting from marketing funnels)
Fulfillment (platform and process development, AI optimization)
Product management (developing the offering and MOAT)
One-to-one coaching and mentoring, platform development and operations
Vili D.
Last position:
Technical Lead, Data Engineer at Mercedes-Benz Consulting
Optimized the data architecture (Medallion) to better decouple processing steps and improve transparency and reproducibility
Ensured the technical quality of data processing in Databricks by introducing schema enforcement, data quality checks, and a structured data architecture
Pipeline orchestration with Azure Data Factory
Improved and automated the development and deployment process by integrating Git and GitHub Actions
Technical leadership of the Data Engineering team (3 employees)
Led workshops to optimize and stabilize the data platform and development process
Collected and prioritized new requests and maintained the product backlog
Technologies: Microsoft Azure (Data Lake, Data Factory), Databricks, Apache Spark (PySpark), Python, SQL, Git, Confluence, Power BI, Power Apps, Dataverse, MS Sharepoint, Mural
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
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.
Matthias S.
Last position:
Technology Lead & Co-Founder at LegalMind GmbH
- Redesign of legal operations: standardised workflows reducing routine effort by up to 80%, with source citation, hallucination check as quality gate, role model, logging and audit trail.
- Compliance-by-design operating model (EU AI Act readiness, GDPR, eIDAS) with documented, releasable process steps.
- Roadmap, sprint planning and release management for an agentic RAG platform with counsel-in-the-loop approval, audit trail and German hosting.
- EU AI Act readiness, GDPR and eIDAS requirements managed as first-class project deliverables; go-to-market for two customer verticals.
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.
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
2.9 years
Positions per freelancer
10
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, Product Development
Bachelor's degree or higher
95%
Master's degree or higher
71%
Doctorate
13%
Certifications per freelancer
3
Most common languages
German, English, French
Speak two or more languages
97%
Based on our profile pool as of 5 Nov 2026.
Daily rate distribution
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.
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 5 Nov 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%)
- Banking and Finance (39%)
- Education (38%)
- Manufacturing (36%)
- 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.
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 769 € 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, 71% 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 (19%).
The most common industries among freelancers who have used Large Language Model in their recent projects are Information Technology (91%), Professional Services (43%), and Banking and Finance (39%).
The most common business areas among freelancers who have used Large Language Model in their recent projects are Information Technology (95%), Product Development (87%), and Project Management (53%).
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.
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