Generative AI Experts
in minutes from over 15,000 CVs with the power of AIHire experts who design GenAI apps, tune large language models, build RAG workflows, and set up evaluation and guardrails, matched fast from vetted, available freelancers.
Meet FRATCH Experts who have recently used Generative AI
Khalid El Mansouri
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 Mahmood
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
Peter Schillen
Last position:
Senior ML Engineer & AI Researcher at Anonymous client
Project: Defect generation on inspection images of metal surfaces
Environment:* Automated Visual Inspection (AVI), Metallurgy & Manufacturing
Goal & implementation: Concept, architecture, and training of 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 inspection-light conditions for privacy-compliant and efficient dataset expansion (Data Augmentation).
Technical design: Implementation of robust Generative AI and Computer Vision pipelines in Python and PyTorch. Use of semantic segmentation approaches for mask-guided defect synthesis and downstream evaluation with EfficientDet object detection models.
Business impact: Massive dataset upscaling (factor of 10x) without time- and cost-intensive physical inspection runs, while at the same time drastically improving the detection performance of automated inspection systems.
Technologies & skills used: Python | PyTorch | SPADE | Pix2PixHD | EfficientDet | Machine Learning | Semantic Segmentation | Computer Vision
Hannah Kaup
Last position:
Lecturer in AI Basic Skills for the Digital Workplace at grandedu
- Lecturer in an AZAV-certified, one-month training program on AI basics and practical application
- Design and delivery of modules for participants from different professional fields
- Teaching how generative AI works, where it can be used, and where its limits are, as well as prompting and evaluation practice
- Creating training materials and exercise formats
- Trainer qualification according to AEVO
Alwin G.
Last position:
IT Interim Manager & AI Strategist
- AI product development: Design of an AI-supported GRC platform to automate compliance processes.
- AI expertise: Strategic deepening in Agentic AI and GenAI as a core asset for modern IT governance
- IT interim management and strategic consulting
Jens Henneberg
Last position:
Interim CTO (occasional assignments) at Fujitsu / FSAS
Stabilizing 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
Michael Nelz
Last position:
Senior AI Engineer | Forward Deployed Engineer at Tiefbau
- Development of an AI-powered project organization tool for a civil engineering company that intelligently links project, task, tender, schedule, and document data through a knowledge graph.
- Implementation of AI features for document analysis, information extraction, context-based assistance, and voice-based data capture based on Microsoft Azure AI, reducing administrative effort, making information available faster, and supporting project teams in decision-making.
- Tech stack: Python, React, TypeScript, FastAPI, Claude Code, Codex, Graphify, PostgreSQL, Microsoft Azure AI Foundry, Azure OpenAI, Azure AI Speech, Azure AI Document Intelligence, Microsoft Graph, Microsoft Entra ID, Docker, Git, CI/CD.
Sebastian Zwiesler
Last position:
CRM Consultant at Adevinta / kleinanzeigen.de
- Goal: Development and optimization of loyalty and CRM measures for a leading e-commerce platform
- Solution: Identification of promising users, increase in engagement, and long-term retention to drive revenue growth
- Definition of local CRM direct communication for specific segments
- Translation of research insights into actionable recommendations
- Planning and implementation of CRM campaigns in close collaboration with marketing and product teams
- Identification of optimization potential in direct communication
- Collaboration with external agencies on creative solutions, prototyping, and UAT
- Measurement and reporting of key KPIs
Franz Bauer
Last position:
Product Development (AI) at Own initiative
AI telephone assistant platform
Claude Code, Google AI Studio, Python, LLM / Voice-AI, PostgreSQL
- Conception and hands-on development of an AI-supported telephone assistant platform (voice AI / LLM) – from idea and architecture to MVP/product.
- Built agentic workflows and full automations with Claude Code and Google AI Studio.
- Also delivered AI-supported work in client engagements: used Claude Code for governance documentation, requirement drafts, and automations.
Karin Albiez
Last position:
AI Benchmark Engineer | Native language specialist German at Lilt
- Task Engineering: Evaluating Coding Agents.
- Asset Creation: Building realistic task environments using datasets and files in German. Crucially, these assets must remain in the target language to genuinely measure multilingual handling.
- Prompting & Translation: finding failure points where AI does not work, in German.
- Implementation & Verification: Supporting the development of robust solutions (reference implementations) and write highly reliable, deterministic verifier scripts (using rubric-based judging only when strictly necessary).
- Calibration & Execution: Analyze execution logs and calibrate task difficulty (Easy to Very Hard) using standard Terminal-Bench run configurations against various model tiers (Haiku, Opus).
- Quality Assurance: Participation in a rigorous, 4-layer human quality control process (creation, human review, calibration review, and audit) alongside automated LLM-based checks to ensure fairness, grammatical accuracy, and benchmark integrity.
- Linguistic Review: Reviewing AI benchmark tasks across Hindi, Arabic, Japanese, Chinese, Czech and Turkish.
Hubertus S.
Last position:
Senior Product Manager AI
Workflow-automation SaaS for operations teams (Berlin, 120 people); full-time freelance engagement reporting to the CEO: an initial 12-month interim mandate, extended twice through the AI build-out; owned product for one squad and coached the other product managers on process.
- Led generative AI (LLM) integration into the core product: from LLM-powered steps to natural-language workflow authoring and step-level automation suggestions, plus AI-managed dynamic workflows, shipped behind eval gates with human-in-the-loop fallbacks: AI-drafted workflows grew to 31% of all new workflows, and median time-to-first-workflow fell from 3 days to 4 hours.
- Packaged the AI capabilities as a usage-based add-on priced on executed automation steps, working with sales and marketing on positioning: ~€800K added ARR in the first year, and adopting accounts churned 1.8 pp less.
- Owned the roadmap end to end: replaced feature-request-driven quarterly planning with an outcome-based rolling roadmap built on quarterly bets and explicit kill criteria, presented monthly to the executive team and quarterly to the board.
- Rebuilt the product-management operating system: weekly customer-discovery cadence incl. workshop facilitation, RFC/decision-doc reviews and a single quarterly metrics narrative; coached four product managers, one promoted to senior during the engagement.
- Closed the engagement as scoped: hired and onboarded the permanent VP Product, handed over the process playbook and roadmap, and exited on schedule in June 2026.
Marijn Scholtens
Last position:
Senior Software Engineer at Puls Security GmbH
Optimizing and acceleraÂtion of our Gitlab CI pipeline
Conceptual work for the PoC of the Zero Trust system
Extension of the policy-engine backend in Go
Extension of the policy-testing mechanism in Python
Architectural design of the PEP component of Zero Trust
Documentation of the product
Technologies: Zero Trust, Go, Python, Gitlab CI, Docker, JWT, Domain-Driven Design
Burhan Dinler
Last position:
Enterprise Architect & Solution Architect at DB Netz AG
With project PRIZMA, DB will modernize its infrastructure on the one hand, and develop a fail-safe IT landscape on the other hand, which can be restored quickly and securely in case of a disaster.
- Capture current architectures of existing systems as well as methodical consulting and development of target architectures
- Deepen and maintain the building plan / target IT landscape
- Implement technical architecture concepts & architecture descriptions
- Implement migration concepts for updating and further developing the platform and information systems
- Assess submitted improvement suggestions as part of the project
- Capability management: identify capability gaps, develop target visions, and support transformation planning within the enterprise architecture.
- Create a compatibility matrix of the components in use and compare dependencies of specific versions
- Create an IT concept for extending the platform with the following topics: hardware and software requirements, security, licensing, high availability, load balancing, backup & recovery, update strategy, monitoring integration, etc.
- Coordinate with business architects as well as technical architects from the cross-functional architecture area of the PRISMA program for the topics (backup, Active Directory, monitoring, Citrix, and business applications ...)
- Status meetings and alignment of project planning with the Release Train Engineer / Project Manager
- Advise the Release Train Engineer / Project Manager in identifying project risks
- Advise the System Architect Engineers in steering the implementation of the concept
- Implement the IT concept
- Document the infrastructure
Label: MS Project, LINUX, Windows, ORACLE, Java, REST, SharePoint, Microsoft Exchange, UML, Enterprise Architect, BPMN, AZURE, AWS, V-MODEL, Micro Service, VisualStudio, SAP S/4HANA, SCRUM(SAFE), ESB (TIBCO), Python, Innovator, LeanIX (TOGAF), Ansible, Ansible Tower, Ansible Automation, ROBOT, SpringBoot
Luca Beck
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
David Schindler
Last position:
Senior Marketing and Communications Consultant at NetCologne Gesellschaft für Telekommunikation mbH
- Managed marketing, content and communications projects with six-figure budgets for a regional telecommunications and IT service provider
- Developed and managed content and video formats from concept to production, including shoot planning
- Led and coordinated social media managers, creative teams as well as external agencies and service providers
- Conceptualized, developed and optimized campaign landing pages throughout the entire lifecycle
- Managed collaboration between departments, management and external partners
- Ensured consistent brand communication as well as timely and high-quality delivery of all projects
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
9
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 6 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range.
Average rates of experts 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 6 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
What it covers
Generative AI means systems that create text, code, images, audio, or structured outputs from prompts and context. Strong professionals use it to build copilots, support assistants, content workflows, search experiences, and internal knowledge tools. The work often starts with a clear use case and ends with reliable output in production.
Common builds
- Chat and voice assistants
- Retrieval-augmented generation with private documents
- Prompt flows and tool use
- Content drafting and summarization
- Code help and workflow automation
Skills that matter
Good specialists understand model behavior, prompt design, evaluation, and safety. They also know how to work with APIs from OpenAI, Anthropic, Google Gemini, and open-source models such as Llama. In practice, they connect model output to product logic, data sources, and review steps.
When companies bring help
Companies usually bring in freelance expertise when they want to move from experiments to a usable system. That includes choosing a model, shaping prompts, building RAG, reducing hallucinations, and defining quality checks. They also need help when an internal team lacks time to compare options or harden a prototype.
What strong experts deliver
Strong professionals do more than write prompts. They test outputs, document limits, set fallback paths, and create repeatable evaluation cases. They also understand where a smaller model, a fine-tuned model, or a retrieval layer is a better fit than a general chatbot.
Good fit for teams
Generative AI work fits product teams, customer support, sales enablement, legal review, knowledge management, and media workflows. For companies in Germany or other multilingual settings, it helps to have experts who can handle English content, local terminology, and on-site or remote collaboration when needed.
Frequently asked questions
Not sure where to start with Generative AI? These answers cover the essentials.
Generative AI is used to draft text, summarize documents, answer questions from private content, and assist with coding or support workflows. It also powers chat experiences, content generation, and structured output from unstructured input. In a company setting, the goal is usually not a demo, but a repeatable workflow that people can trust.
Generative AI creates new output, while classic machine learning often classifies, predicts, or scores existing data. That makes GenAI a better fit for drafting, conversation, and knowledge retrieval, but not always for every decision task. A strong specialist knows when a simpler model is safer, cheaper, or easier to maintain.
GenAI is broader than large language models. LLMs are one major part of it, but the field also includes image generation, speech generation, multimodal systems, and tool-using agents. If your project is only text-based, an LLM may be enough; if it spans media types, the scope is wider.
A strong Generative AI specialist usually understands prompt design, API integration, data access, evaluation, and security basics. For many projects, they also need experience with Python, vector databases, retrieval workflows, and product thinking. If the system must be reliable, testing and monitoring matter as much as model choice.
A small proof of concept may only need a Generative AI expert who can set up prompts, models, and basic evaluation. Production work needs more depth: data handling, guardrails, failure handling, and a clear release process. If the output affects customers, support teams, or internal decisions, choose someone who has shipped real systems.
A Generative AI specialist helps compare hosted APIs with open-source models like Llama. Hosted models are faster to start with, while open-source options can offer more control over data, deployment, and tuning. The right choice depends on privacy needs, performance goals, and how much operational work your team can support.
Yes, Generative AI work is often remote-friendly because much of it depends on shared prompts, datasets, tests, and review cycles. On-site time can help when teams need to map workflows, sensitive content, or internal knowledge sources. In multilingual environments such as Germany, clear written communication is especially important.
Judge a Generative AI expert by the quality of their evaluation approach, not by flashy demos. Look for clear tests, sensible model choices, honest trade-offs, and evidence that they can reduce bad outputs, not just create them. Good freelancers explain limits, document decisions, and design systems that still work when prompts change.
The average hourly rate of freelancers who have used Generative AI in their recent projects is 102 €, which corresponds to a daily rate of about 812 € 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 (99%), 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 (48%), and Automotive (41%).
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 (61%).
Main locations of FRATCH Experts, who have recently used Generative AI
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
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Dusseldorf 
Dortmund 
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Philipp Thomaschewski
FRATCH CEO
