Generative AI Experts
to build intelligent products with vetted and available freelancers matched in minutesHire experts who design LLM applications, retrieval-augmented generation systems and production-ready AI workflows. FRATCH connects you with vetted, available freelancers whose skills match your Generative AI project precisely and quickly.
Meet FRATCH Experts who have recently used Generative AI
Alwin G.
Last position:
IT Interim Manager & AI Strategist
- Founder of CheironX: AI-supported GRC management (ISO 27001, BSI IT-Grundschutz, TISAX, DORA)
- Strategic focus on Agentic AI and GenAI for modern IT Governance, Risk & Compliance Management
- IT interim management and strategic consulting
Reinhold M.
Last position:
Business Requirements Engineer, Proxy Product Owner, Tester at AXA Konzern AG
Working Environment
- Insurance / Claims Management (Property, Liability, Automotive)
- REST-based ClaimAPI with Guidewire ClaimCenter
Scope
- Further development of the internal REST-based ClaimAPI with Guidewire ClaimCenter as the backend and various portals/front-end offices as clients, and with the following objectives: + increased AI-supported claims processing outside of Guidewire ClaimCenter + and support for all lines (property, liability, automotive).
- Further development of automated claims processing
Activities
- Interfacing IT & Business
- Design activities with documentation in Confluence, Jira: + Requirements engineering (functional and non-functional) + Design of REST endpoints and coordination with interface partners (esp. AI-colleagues) + Identification, design and evaluation of alternative solutions (moving away from a monolithic approach toward fine-grained endpoints, with code reuse across all lines if possible) + Creation / refinement of product backlog items in Jira, i.e. epics, user stories and tasks, incl. alignment with developers & POs during (pre-)refinement sessions + Using AXA Secure GPT when creating / refining user stories (description, acceptance criteria) and designing REST endpoints + Estimation and prioritization of user stories and tasks during Sprint Planning sessions + Coordination and support during the implementation of Product Backlog items + Driving decision making with preparation and presentation of decision-making documents
- Quality assurance: + Test management, including test case creation using Xray in Jira + Test execution using Bruno as REST client and in Guidewire ClaimCenter + Submission of test artifacts for PO approval + Analysis of production bugs
Methods Big Room Planning (aka PI-Planning in SAFe), Scrum, BPMN, EPK, UML, ERM, DDD
Tools / Tech Stack Teams, Confluence, Jira with Xray, OpenAPI/Swagger, REST, JSON, XML, MS Office, Bruno, GitHub repository (reading), Guidewire ClaimCenter, AXA Secure GPT (AXA's in-house GenAI solution based on Azure OpenAI)
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
Roland C.
Last position:
Founder, Agents in Everyday Business at CXO AI OS
CXO AI OS is an agent system made up of six building blocks. Instead of using AI as a chat window, it creates a system that knows a company's context, makes decisions according to its rules, and acts on its behalf.
- For SMEs: guided sprint followed by ongoing operation for a team, department or prioritized cluster, based on an AI assessment
- For self-employed people: program in which participants build their own agent system
- Sequence in the company: assessment, prioritization, sprint, operation
- Implementation in Claude Cowork or ChatGPT Work, without programming
- Architecture: Chief of Staff, Goals, Advisors, Agents, Context, Catalog
Hannah K.
Last position:
Lecturer in AI Fundamentals for the Digital Workplace at grandedu
- Lecturer in AZAV-certified, one-month training programs on AI fundamentals and practical applications, with several cohorts since February 2026
- Design and delivery of all modules for participants from different professional fields
- Teaching how generative AI works, its areas of application and limitations, as well as prompting and evaluation practices
- Created all teaching materials and exercise formats independently
- Supporting participants throughout the entire course period
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
Yulia V.
Last position:
Independent Advisor - Strategy, Transformation & AI at JV Advisory
Independent advisory practice for strategy, organisational transformation, M&A / investor work and AI/data-enabled change.
- Short-term diagnostic and problem-solving mandates: rapid strategic and organisational assessments for leadership teams and Boards, translated into recommendations and transformation roadmaps.
- Organisational redesign and AI governance for an international group of approximately 1,500 employees: structure, mandates, decision rights and interfaces; AI-readiness and data-maturity assessment; vendor evaluation and selection; capability building.
- Design of a centralised Strategy Execution / Results Delivery Office for an industrial client: mandate, governance, portfolio steering, KPI logic, decision forums and implementation roadmap.
- Selected part-time mandate with SPARS GmbH (as a Business Development and Investments Director): developed M&A pipelines, assessed targets for strategic fit and synergy potential, led commercial due-diligence workstreams and investor / partner engagement.
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
Volker W.
Last position:
Analyst – Platform Optimization at TradingView ProRealtime
Programming custom indicators with ProBuilder (for the ProRealTime platform)
6 months at RCP - Rabe Academy (basics - trading account - TWS Interactive Brokers) Focus: ETH price history - NVIDIA stock (NASDAQ and CBOE)
- 500 hours total (TradingView & ProRealTime), including approx. 1,500 hours of tick data analysis using ProRealTime
Systematic market analysis, development of custom indicators, pattern recognition.
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.
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.
Vadim R.
Last position:
Independent AI Product Lab – Agentic Product Owner / Product Builder | R&D
- Hands-on development of AI-native product prototypes with specialized AI agents for research, requirements, business logic, UX/flow design, test case generation and quality assurance.
- Structured use and orchestration of AI agents through clearly defined roles, inputs/outputs and handover points; breaking down complex product tasks into verifiable work packages and iterative prototyping cycles.
- Establishment of human-in-the-loop quality gates to validate AI-generated results for functional correctness, consistency, completeness and feasibility; targeted rework cycles in case of deviations.
- Development of a regulatory GenAI/rules prototype for CRD VI with a structured decision flow, web UI, rule-based validation and automated test cases; iteration of the business logic through to a pilot-ready POC.
- Design of an AI-to-Action banking prototype: AI intent → consent → bank/product logic → conversion including admin console; translating the product idea into MVP scope, role model, user flows and clickable prototypes.
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
Khalid E.
Last position:
Lead Architect & Developer at kem-consulting
Development of an agent-based governance platform for the automated assurance of EU AI Act compliance and ODA-compliant orchestration of AI services in complex enterprise environments.
Design and implementation of an agent-based "Mission Control" framework (Aletheia Conductor) for autonomous state monitoring and process control.
Development of "Compliance-as-Code" (CaC) solutions based on OPA/Rego for system-wide enforcement of regulatory guardrails.
Integration of TM Forum ODA standards (TMF630, TMF622, TMF642) to ensure interoperability and standardization.
Building a highly available event-driven architecture using Redpanda and CloudEvents v1.0 for near-real-time event processing.
Implementation of an audit-proof "Evidence Chain" through cryptographic linking of trace logs in preparation for automated audits.
Tech Stack: Java 21 (Quarkus Native), TypeScript (Next.js), Redpanda (Kafka API), CloudEvents v1.0, OPA (Open Policy Agent) & Rego, TimescaleDB, ZincSearch, Redis, TM Forum ODA, Git, GitHub, Clean Code Development, Like-C4.
Peter S.
Last position:
Senior ML Engineer & AI Researcher at Anonymous Client
Project: Defect Generation on Test-Bench Images of Metal Surfaces Environment: Automated Visual Inspection (AVI), Metallurgy & Manufacturing
- Objective & Implementation: Designed, architected, and trained Generative Adversarial Networks (Pix2PixHD / SPADE) for image-to-image transformation. Targeted generation of synthetic material defects (e.g., cracks, inclusions, scale) on rough metal surfaces under real test-bench lighting conditions for privacy-compliant and efficient dataset expansion (data augmentation).
- Technical Design: Implemented robust Generative AI and computer vision pipelines in Python and PyTorch. Used semantic segmentation approaches for mask-controlled defect synthesis and subsequent evaluation with EfficientDet object detection models.
- Business Impact: Massive dataset upscaling (10x) without time-consuming and costly physical test-bench runs, while significantly improving the detection performance of automated inspection systems.
Technologies & Skills Used: Python | PyTorch | SPADE | Pix2PixHD | EfficientDet | Machine Learning | Semantic Segmentation | Computer Vision
Discover over 15,000 top freelancers
Statistics of experts using Generative AI
Aggregated from the professional profiles of matched freelancers.
Experience
16 years
Position duration
2.3 years
Positions per freelancer
10
Top business areas
Information Technology, Product Development, Project Management
Top industries
Information Technology, Professional Services, Automotive
Certification focus areas
Information Technology, Project Management, Product Development
Bachelor's degree or higher
96%
Master's degree or higher
75%
Doctorate
16%
Certifications per freelancer
3
Most common languages
English, German, French
Speak two or more languages
97%
Based on our profile pool as of 16 Oct 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 Generative AI
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 16 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Generative AI 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 (89%)
- Professional Services (48%)
- Automotive (40%)
- Banking and Finance (39%)
- Manufacturing (38%)
- Education (35%)
- Retail (33%)
- Healthcare (31%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Generative AI does
Generative AI creates new text, images, audio, video and software output from learned patterns. Companies use GenAI to power assistants, search experiences, content workflows, document analysis and creative tools. Unlike conventional automation, it can respond to open-ended requests while still requiring careful controls for accuracy, safety and traceability.
Models and architecture
Projects may use large language models, multimodal models or image-generation systems from providers such as OpenAI, Anthropic, Google and open-source communities. Strong solutions connect models to business data through embeddings, vector databases and retrieval-augmented generation. The architecture also covers prompt design, model selection, context handling, fine-tuning, tool calling and structured output.
Delivery and tooling
Generative AI work spans experimentation, integration and production operations. Specialists commonly work with Python, TypeScript, REST APIs, orchestration frameworks, cloud services, data pipelines and evaluation tooling. They establish versioned prompts, reusable components, observability and safeguards rather than treating a model endpoint as a complete product.
Where companies use it
- Internal knowledge assistants grounded in company documents
- Customer support systems with controlled access to business data
- Contract, invoice and report extraction with human review
- Marketing, product and media workflows with brand controls
- Coding, testing and research support tools
Generative AI can support many industries, from financial services and healthcare to manufacturing, retail and public-sector operations. The right design depends on data sensitivity, response risk, user expectations and the level of human oversight required.
When freelance expertise helps
Companies often bring in freelance specialists when they need to validate a use case, connect a model to proprietary information or move a promising prototype into reliable production. They may also need support with model evaluations, latency, cost control, access policies or migration between providers. A specialist is especially useful when internal teams lack experience with GenAI failure modes and governance.
What strong professionals bring
Experienced Generative AI professionals define measurable quality criteria before selecting a model. They test factuality, relevance, refusal behavior, data leakage and robustness against adversarial input. They understand both product delivery and the limits of foundation models, communicate trade-offs clearly and design systems that keep people involved where automated output carries material risk.
Frequently asked questions
Not sure where to start with Generative AI? These answers cover the essentials.
Generative AI is used to create or transform text, images, audio, video and software output. Common applications include knowledge assistants, document processing, customer support, content production and research workflows. The best use cases have clear data boundaries and a way to review or measure the output.
Generative AI handles open-ended language and media tasks, while traditional automation follows predefined rules and produces more predictable results. GenAI is useful when inputs vary or the required response is difficult to encode as fixed logic. It also introduces risks such as hallucinations, inconsistent wording and privacy exposure, so it often works best alongside deterministic controls.
Generative AI projects do not always need the largest available large language model. A smaller model, retrieval system, classifier or conventional machine learning method may be better for a narrow task, strict latency requirement or sensitive environment. A capable specialist compares quality, context needs, deployment constraints and operating effort before recommending a model.
A strong Generative AI specialist often combines prompt and evaluation design with data engineering, API integration, cloud architecture and security practices. Useful adjacent knowledge includes vector databases, embeddings, retrieval-augmented generation, model observability and user-experience design. Familiarity with Python or TypeScript is common, but the required stack depends on the product.
The right level for Generative AI depends on the risk and scope of the work. A simple prototype may need focused model integration, while a production system handling private data requires experience with evaluations, access controls, monitoring and failure recovery. Ask candidates to explain comparable deliverables and the trade-offs they made, not only the models they have used.
Generative AI work is often suitable for remote collaboration because model APIs, code, evaluations and documentation can be shared securely online. On-site work may add value when specialists must understand sensitive processes, coordinate closely with operational teams or work with restricted infrastructure. Agree early on data access, communication routines and language expectations.
A reliable Generative AI solution should be tested against representative tasks rather than judged by a compelling demo. Review factuality, relevance, safety, response time, data handling and behavior on unclear or adversarial requests. Strong professionals document evaluation methods, known limits, human review points and how quality will be monitored after launch.
Before starting a Generative AI engagement, clarify the target users, source data, model constraints, success criteria and responsibility for reviewing output. Ask whether the project needs retrieval, fine-tuning, tool use, multimodal input or a simple API integration. It is also important to understand privacy requirements, deployment ownership and how changes to a provider's model will be handled.
The average hourly rate of freelancers who have used Generative AI in their recent projects is 101 €, which corresponds to a daily rate of about 811 € based on an 8-hour working day.
Of the freelancers who have used Generative AI in their recent projects, 96% hold at least a Bachelor's degree, 75% hold at least a Master's degree, and 16% hold a doctorate.
On average, freelancers who have used Generative AI in their recent projects have 16 years of professional experience, with a single engagement typically lasting around 2.3 years.
The most common languages among freelancers who have used Generative AI in their recent projects are English (98%), German (97%), and French (20%).
The most common industries among freelancers who have used Generative AI in their recent projects are Information Technology (89%), Professional Services (48%), and Automotive (40%).
The most common business areas among freelancers who have used Generative AI in their recent projects are Information Technology (89%), Product Development (85%), and Project Management (63%).
Main locations of FRATCH Experts, who have recently used Generative AI
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.
Berlin 
Hamburg 
Munich 
Cologne 
Frankfurt 
Stuttgart 
Dusseldorf 
Dortmund 
Essen 
Nuremberg 
Vienna 
Zurich Countries:
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