Generative AI Experts
matched in minutes with vetted, available freelancers and the power of AIHire experts who design retrieval-augmented generation systems, fine-tune language models and integrate tools such as OpenAI, Anthropic and Hugging Face. FRATCH matches you quickly and precisely with vetted, available freelancers for your Generative AI project.
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
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
Florian S.
Last position:
AI Product Manager / Product Owner at AI Product
- Generative AI products for corporate clients, owned from strategy through specification to production.
- Central strategy, local configuration: multi-tenant AI assistant for occupational pension schemes (bAV), delivered as an interactive avatar with text and voice path. Three tenants run on one codebase, each with its own conversation guide, while the knowledge base, guardrails and escalation paths stay central
- Versioned, AI-ready knowledge base composed into a tenant-agnostic voice context and tenant-specific text prompts — the configuration layer that keeps local adaptation from forking the product
- Conversational design: answer limits, scope and off-topic handling, anti-hallucination rules, escalation and lead handover to human advisors
- Five eval suites as a quality gate before any prompt or model change (anti-hallucination, LLM-as-judge failure modes, multi-turn consistency, voice KPIs, action vocabulary with confusion matrix); user test with 10 testers (Hamburg, 07/2026) drove the rework from alpha to beta
- Coordinated external developers, compliance and client stakeholders; GDPR-compliant EU stack, IDD-compliant, EU AI Act classification documented
- Second product line: white-label social media generator for consultancy chilli mind (CH/DE) — one codebase, per-client branding and configuration
- Results: 239+ deployments and a pilot with corporate customers · 108+ deployments for the white-label product · repeatable pattern for multi-tenant AI products in a regulated environment
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.
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 application, with several sessions since February 2026
- Designed and delivered all modules for participants from a range of professional backgrounds
- Teaching how generative AI works, its areas of application and limitations, as well as practical prompting and evaluation
- Created all teaching materials and exercises independently
- Supported participants throughout the entire course period
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.
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
Roland C.
Last position:
Founder, Agents for Day-to-Day 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 understands a company’s context, makes decisions according to its rules, and acts on its behalf.
- For mid-sized companies: a guided sprint followed by operation for a team, department, or prioritized cluster, based on an AI assessment
- For self-employed professionals: a 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 coding
- Architecture: Chief of Staff, Goals, Advisors, Agents, Context, Catalog
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
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.
Shamaila M.
Last position:
Founder/Kubernetes and Cloud Architect at Kubekanvas
- Developed a browser-based platform for Kubernetes no-code deployment and cluster management
- Developed a CLI in TypeScript to deploy 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 and permission models for Keycloak, OAuth/OIDC, and social login flows.
- Used LLMs to convert user intent into diagrams.
- Worked on integration with multiple sovereign clouds like StackIT, Hetzner, CIVO, UpCloud, plus public clouds like AWS, GCP, and Azure
- The technology stack includes Java, Spring Boot, Kubernetes, OpenAI, Kubernetes multi-tenancy using vCluster, Karpenter, RBAC for CLI, Helm, React
Jens H.
Last position:
Interim CTO (occasional assignments) at Fujitsu / FSAS
Stabilization of an Azure/.NET landscape in live operation.
- Architecture, DevOps, and operational readiness; technical decisions under time pressure
- Azure DevOps, monitoring, ETL/ELT, cloud security, FinOps, and data-mesh-related topics
Technologies: Azure DevOps, .NET, CI/CD, monitoring, FinOps
Sergei M.
Last position:
Interim Program Manager at parcIT
Time-limited interim assignment filling a vacant program leadership position: joint leadership of a complex software project and orchestration of several agile teams (business, development, and DevOps teams), as well as actively driving change processes.
- Program Management & Planning: Responsibility for creating and updating milestone, resource, and budget plans to ensure project objectives using agile and hybrid methods.
- Cross-functional Collaboration: Promoting cooperation and knowledge sharing across teams; resolving blockers through proactive conflict management and targeted facilitation.
- Stakeholder Management & Transparency: Ensuring transparent and audience-appropriate communication within and outside the project organization using modern project management tools.
Luca B.
Last position:
Founder & CEO at Lube AI
- Develop custom AI agents delivering 90%+ reduction in manual workload and significant efficiency gains
- Provide end-to-end AI strategy consulting: from digital assessment to implementation and change management
- Design and deliver tailored training programs and workshops on AI adoption, prompt engineering, and automation
- Support clients in implementing scalable AI solutions integrated with existing technology stacks
- Focus areas: AI strategy, automation, workflow optimization, and capability building
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, Product Development, Project Management
Bachelor's degree or higher
97%
Master's degree or higher
74%
Doctorate
16%
Certifications per freelancer
3
Most common languages
English, German, French
Speak two or more languages
98%
Based on our profile pool as of 26 Sep 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 26 Sep 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 (88%)
- Professional Services (47%)
- Automotive (40%)
- Banking and Finance (38%)
- Manufacturing (36%)
- 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 content from learned patterns in text, images, audio, video and structured data. Companies use it for customer support, document processing, search, software assistance, marketing content and internal knowledge tools. A successful system combines a capable model with reliable data, controls and a useful product experience.
Models and ecosystems
Projects may use foundation models from OpenAI, Anthropic, Google or open-source communities through Hugging Face. Specialists work with large language models, multimodal models, embeddings, vector databases and inference APIs. They also select between hosted services and self-managed models based on privacy, latency, cost and control requirements.
- Prompt design and structured model outputs
- Retrieval-augmented generation with trusted sources
- Fine-tuning, evaluation and model routing
- Embeddings, vector search and tool calling
Where it is applied
Generative AI appears in products and operations that depend on language, knowledge or creative output. Common deliverables include conversational assistants, enterprise search, document extraction, recommendation features and workflow automation.
- Support assistants grounded in company content
- Contract, invoice and report analysis
- Content and campaign production workflows
- Code explanation and software team assistants
- Voice, image and multimodal user experiences
When freelance expertise helps
Companies often bring in a specialist when a proof of concept must become a dependable product. External expertise is useful when teams need to compare models, connect private data, establish evaluation methods or address security and governance concerns. It can also accelerate a focused use case without committing to a permanent internal team.
Delivery and adjacent skills
Strong projects connect model behavior to production systems. Relevant skills include Python or TypeScript, API design, cloud infrastructure, data pipelines, observability, identity management and frontend integration. Specialists should understand prompt injection, data leakage, copyright questions, human review and fallback behavior, not only model selection.
What strong specialists deliver
Good professionals define measurable outcomes before choosing a model. They create representative test sets, inspect failure cases and document prompts, sources, permissions and deployment decisions. They make responses traceable where possible, protect sensitive data and design for graceful failure. Clear communication matters because Generative AI projects involve product, legal, security and domain teams.
Frequently asked questions
Not sure where to start with Generative AI? These answers cover the essentials.
Generative AI is used to create and transform text, images, audio, video and structured information. Typical projects include support assistants, enterprise search, document analysis, content workflows and software tools.
Generative AI produces new content and can handle open-ended language or multimodal tasks, while traditional machine learning often predicts a defined label, score or value. The right choice depends on the output required, the available data and how much control the application needs.
A strong Generative AI specialist may also work with Python, TypeScript, APIs, cloud services, data engineering, vector databases and frontend integration. Security, evaluation, product design and knowledge of a company’s domain are equally important for production work.
The right level depends on the risk and scope of the project. A simple prototype may need focused model and API knowledge, while a production system needs proven experience with evaluation, retrieval, permissions, monitoring and failure handling.
Most Generative AI work can be delivered remotely through shared repositories, cloud environments, documentation and regular reviews. On-site collaboration can help when specialists must work closely with confidential data, operational teams or workshops, but it is not required for every project.
Ask how the Generative AI professional measures factuality, relevance, safety and latency rather than relying on impressive demonstrations. Review their approach to representative test data, source citation, privacy, prompt injection, human review and behavior when the model does not know an answer.
An open-source model can offer more control over deployment, data handling and customization, while a hosted API may reduce operational work and provide access to highly capable models. The decision should consider privacy, infrastructure, latency, licensing, model quality and the team’s ability to operate the system.
A Generative AI freelancer may define the use case, select models, build retrieval or tool integrations, create evaluation sets and connect the system to existing software. Depending on the engagement, they may also document risks, tune prompts, prepare deployment and transfer knowledge to the internal team.
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 804 € based on an 8-hour working day.
Of the freelancers who have used Generative AI in their recent projects, 97% hold at least a Bachelor's degree, 74% 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 (88%), Professional Services (47%), 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 (62%).
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 
Nuremberg 
Vienna 
Zurich Countries:
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