OpenAI Experts
in minutes from over 15,000 CVs with the power of AIHire experts who build with OpenAI API, ChatGPT, function calling, and custom assistants. They design safe prompts, connect your data, and ship production workflows fast with vetted, available freelancers.
Meet FRATCH Experts who have recently used OpenAI
Stefan Ojanen
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.
Onur Kayir
Last position:
Project Manager & Outsourcing Manager at SENEC GmbH (EnBW Group)
- Setup of a scalable nearshore IT developer hub (Croatia, Czech Republic, Poland) as an independent company through a BOT model (Build – Operate – Transfer)
- Identification, selection, and management of full-service agencies; introduction of control and governance mechanisms including KPIs, SLAs, and regular service reviews
- Creation and review of data processing agreements and framework contracts in alignment with Legal & Compliance; integration of regulatory requirements (incl. KRITIS) into process design
- Consulting on cloud vs. on-premise strategies, data storage, and authorization concepts; support for procurement in vendor selection and provider assessments
- Change management and process harmonization between internal teams and nearshore partners; reporting to management, CFO, and CIO
Result: Scalable IT developer hub with an audit-proof governance model, reduced operating costs, and faster product development.
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
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
Fadi Shoaa
Last position:
Development of a production-ready Enterprise Document AI & Recommendation Platform at Freelancer
- Development of a production-ready Enterprise AI solution for the automated processing of invoices and business documents
- Integration of Azure AI Document Intelligence and LLM technologies into existing business processes
- Development of robust REST APIs for automated document processing and system integration
- Extraction, validation, and storage of structured invoice data in Azure SQL as a base for analytics and machine learning models
- Development of an AI-based recommendation engine with machine learning and deep learning to generate personalized product recommendations based on historical purchase data
- Implementation of logging, monitoring, error handling, and validation mechanisms for stable production use
- Collaboration with business teams to define business rules and integrate the solution into existing enterprise processes
Technologies: Python, Azure AI Document Intelligence, Azure OpenAI, Azure SQL Database, REST APIs, Machine Learning, Deep Learning, OCR, Pandas, JSON, Workflow Automation
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.
Fred Hauschel
Last position:
Software Architect and Developer at Personal project
Recurring problem in my own AI-supported projects: requirements analysis, use cases, and architecture decisions can be created quickly with AI support, but they remain hard to trace and are scattered across markdown files – knowledge is lost as soon as it is no longer in the context window. arknet turns requirements engineering and architecture knowledge into structured, testable data instead of plain text: requirements, use cases, and architecture decisions as a consistently linked knowledge graph, traceable from the requirement to the architecture decision – queryable for humans and AI agents alike. Built technically on RDF/OWL and its own MCP server.
Result: MCP daemon running, Docker image automatically published on GHCR, nine hexagonal modules, eleven ADRs (including an open-core license model). Requirements engineering and ubiquitous language hexagon active. Publicly available as a Community Edition under Apache-2.0 since 07/2026 (github.com/kogn-io/arknet), together with the Claude Code plugin and the GHCR image; open-core model.
Label: Java, Maven, RDF, RDF4J, OWL, SPARQL, Model Context Protocol, Spring AI, Docker, GitHub, Git, Claude Code, Obsidian, DDD, Hexagonal Architecture, ArchUnit, JUnit, AssertJ, interface development, Software Architecture, Continuous Integration, Knowledge Management
Marcus Biel
Last position:
Java and Quarkus Expert at Large German energy service provider
- Modernization of a large-scale Java enterprise application*
The project is modernizing a complex enterprise application that has grown over many years. The existing Spring-based legacy system runs on Java 8, OSGi, and Eclipse RCP and is being gradually migrated to a modern, maintainable architecture with Java 25 and Quarkus.
Marcus works on analysis, architecture, refactoring, and implementation. One focus is on untangling historically grown structures and dependencies and on building a clean, sustainable Java and Quarkus technology stack.
Tools & technologies: Java 8, Java 25, Quarkus, Hibernate ORM with Panache, EclipseLink, OSGi, Eclipse RCP, Maven, JUnit, Mockito, REST, JSON, Git, Eclipse IDE, IntelliJ IDEA Ultimate, Jira, Confluence
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
Patrick Horn
Last position:
Developer & Operator at OXO UG
Seitenkumpel — agents build websites for trade businesses, unattended. Own product, live.
- Agents research public company data and build complete websites from it, with nobody watching
- A validation layer makes sure extraction errors fail loudly instead of passing quietly
- Acquisition runs through a postcard funnel with a screenshot and a QR code, subscription model from 79 euros a month
- Result: several hundred websites built, running unattended
- Honest limit: there are no paying subscriptions yet — the funnel is built, the revenue is not there
Stack: agent workflows built directly without a framework, Claude and OpenAI APIs, TypeScript, Node.js, PostgreSQL, Cloudflare Workers, web scraping, data enrichment
Boris Solos
Last position:
Generalist expert for software development at Mercor
- Training AI models, evaluating images and text UI/UX, turning the provided data into insights through OpenAI Feather as part of the machine learning workflow
Technologies: OpenAI Feather
Niklas Witzel
Last position:
AI Engineer at Tensora GmbH
- Designed and developed a multi-tenant SaaS platform enabling organizations to build their own knowledge bases and chat with brand-customized AI assistants (white-label approach with dynamic branding per organization).
- Implemented a scalable RAG architecture with a GPT-4o tool-use loop, hybrid semantic search, and strict tenant isolation at database and search index level.
- Built persistent, project-like chat sessions including a streaming API (SSE), multilingual support, and speech input/output (STT/TTS).
- Delivered the cloud infrastructure as Infrastructure-as-Code, fully automated per-customer CI/CD pipelines, and an onboarding process for new tenants.
Technologies used: Python, FastAPI, Pydantic (v2 noted), Next.js, React, TypeScript, Tailwind CSS, OpenAI / LLMs (GPT-4o), Azure AI Search, Cosmos DB, Azure Blob Storage, Azure Cognitive Services Speech, Azure App Service, Azure Container Registry, Retrieval-Augmented Generation (RAG), Server-Sent Events (SSE), Docker, Terraform, GitHub Actions, REST, OpenID Connect (OIDC), Multi-Tenancy
Osman Tartoussi
Last position:
Senior Architect, DevOps Engineer at genPsoft GmbH
IT consulting, analysis, architecture design, new and further development, code review, test automation, continuous integration, continuous delivery in backend and frontend areas for Automotive Project Instavalo.
Frontend:
- Implementation of UI components according to specifications, especially style guides and responsive design eith React and Typescript
- Component testing
- Code documentation
- CI/CD with Gitlab Pipeline
Backend / IoT:
- Analysis and architectural design with AWS Greengrass IoT on Edge Devices
- Setting up Microservices containers with Docker Compose on Edge device with AWS Greengrass and AWS IoT IAM, Token Exchange Service, Ansible
- CI/CD with Gitlab Pipeline, Terraform, AWS ECR
- Logging with Fluentbit Lua Language for AWS Cloudwatch
- Python Lambda for AWS Greengrass Recipe deployment on Edge Devices
- Implementation of test-driven development with JUnit, Mockito, and code Coverage
- Jacoco
- Definition of REST interfaces with OpenAPI / Swagger
- Development and enhancement of software based on Java Quarkus, Typescript NestJs NodeJs and Python
- Authentication and authorization in Aws IAM
- Development of REST and gRPC interfaces for the frontend and backend
- Implementation of Maven dependencies with DevSecOps OWASP
- Spring AI, Jetbrains AI Assistant, Junie, Github Copilot, Claude Code, Agents, Skills, Command, Hooks, Subagents
Oliver Kierepka
Last position:
Founder & Manager at ThinkForm Studio – AI Product Design & Innovation
Designing AI-native digital products by combining product strategy, UX research, interaction design, software engineering, and modern AI workflows. Leading projects from discovery to implementation while integrating AI throughout the entire product development lifecycle.
Key responsibilities
- → Product discovery, stakeholder workshops, Jobs-to-be-Done and user research
- → User journey mapping, information architecture and interaction design
- → Wireframes, high-fidelity UI, prototypes and scalable design systems in Figma and Penpot
- → AI-assisted interface generation and rapid concept exploration using Figma AI, Figma Make and generative design workflows
- → Design-to-code workflows with AI-supported frontend generation and engineering collaboration
- → Building accessible interfaces following WCAG 2.2 and enterprise design standards
- → Usability testing, iterative validation and KPI-driven product optimization
- → Development of AI knowledge systems, MCP-powered design workflows and human-in-the-loop review processes
- → Close collaboration with engineering teams to ensure production-ready implementation
Discover over 15,000 top freelancers
Statistics of experts using OpenAI
Aggregated from the professional profiles of matched freelancers.
Experience
15 years
Position duration
2.8 years
Positions per freelancer
12
Top business areas
Information Technology, Product Development, Project Management
Top industries
Information Technology, Banking and Finance, Professional Services
Certification focus areas
Information Technology, Product Development, Project Management
Bachelor's degree or higher
90%
Master's degree or higher
62%
Doctorate
10%
Certifications per freelancer
3
Most common languages
German, English, 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 OpenAI
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
OpenAI work usually means building with the OpenAI API, ChatGPT, and the newer model and tool features around them. Companies use it for assistants, search over internal content, drafting, classification, summarization, and workflow automation. The best specialists know how to turn model output into something stable and useful in real products.
Typical builds
- Chat-based assistants for support, sales, or operations
- Prompted workflows for text generation, extraction, and routing
- RAG setups that combine OpenAI with company knowledge bases
- Tool use and function calling for actions across systems
- Review layers for safety, quality, and human approval
Skills that matter
Strong OpenAI professionals understand prompts, embeddings, retrieval, token limits, and response shaping. They also know how to handle rate limits, cost control, and evaluation so the system stays reliable. The best experts write clean integration code and can explain trade-offs in plain language.
Ecosystem fit
OpenAI projects often sit inside a stack that includes Python or JavaScript, vector databases, document pipelines, and product analytics. Many teams also connect ChatGPT-style interfaces to CRM, ticketing, search, or content systems. Good experts choose the simplest path that fits the use case instead of adding complexity early.
When to bring help
Bring in freelance expertise when a proof of concept needs to become a real product, when prompt quality is inconsistent, or when internal teams need help with integration. It also helps when a company wants to compare ChatGPT, OpenAI API usage, and other model options before committing. Remote work fits most OpenAI tasks well, while on-site time can help with workshops and stakeholder alignment.
What strong experts deliver
Strong OpenAI specialists do more than write prompts. They define use cases, test outputs against real examples, add guardrails, and document how the system should be maintained. They also help teams judge whether a feature should use OpenAI, a different model, or a simpler non-LLM approach.
Frequently asked questions
Curious about OpenAI? Here are the answers that come up again and again.
Companies usually hire OpenAI experts to build assistants, automate text-heavy workflows, and add model-based features to existing products. Common work includes ChatGPT-style interfaces, document extraction, internal search, and tool calls that trigger actions in other systems. The best specialists focus on business tasks, not just prompts.
No. OpenAI is the company and model provider, while ChatGPT is the product many people use in the browser or app. In project work, the term OpenAI often refers to the API, model access, and related tooling used inside custom software.
OpenAI is often chosen when teams want a broad ecosystem, strong tool use, and a familiar path from prototype to production. Anthropic and Google Gemini can also be good options depending on the task, data setup, and product goals. A strong specialist can compare them on output quality, integration fit, and operational needs rather than brand name alone.
A solid OpenAI specialist usually brings Python or JavaScript, API integration, prompt design, and basic data handling. For serious production work, retrieval design, evaluation, observability, and security awareness matter too. If the project uses internal content, search and document processing skills are especially useful.
A small prototype may need only one experienced OpenAI specialist, but production work usually needs someone who has shipped real integrations before. The harder parts are not only prompts; they are testing, failure handling, cost control, and safe rollout. If the feature affects customers or operations, choose someone who has worked on live systems.
Most OpenAI projects can be delivered remotely because the work is digital and easy to review in shared environments. On-site sessions can still help for discovery, policy discussions, and prompt review with product or legal stakeholders. Many teams use a mix of remote delivery and short in-person workshops.
Look for clear examples of shipped OpenAI work, not just prompt samples. Good experts can explain how they test outputs, reduce hallucinations, handle edge cases, and decide when a model is the wrong tool. Ask for a short plan, a few real examples, and the way they measure whether the feature helps users.
Before launch, a ChatGPT or OpenAI project should have a clear use case, sample inputs, expected outputs, and a review path for risky results. It should also define where data comes from, what the model can and cannot do, and how users can correct mistakes. That preparation saves time later and makes the system easier to trust.
The average hourly rate of freelancers who have used OpenAI in their recent projects is 95 €, which corresponds to a daily rate of about 756 € based on an 8-hour working day.
Of the freelancers who have used OpenAI in their recent projects, 90% hold at least a Bachelor's degree, 62% hold at least a Master's degree, and 10% hold a doctorate.
On average, freelancers who have used OpenAI in their recent projects have 15 years of professional experience, with a single engagement typically lasting around 2.8 years.
The most common languages among freelancers who have used OpenAI in their recent projects are German (98%), English (98%), and French (15%).
The most common industries among freelancers who have used OpenAI in their recent projects are Information Technology (94%), Banking and Finance (47%), and Professional Services (43%).
The most common business areas among freelancers who have used OpenAI in their recent projects are Information Technology (96%), Product Development (90%), and Project Management (56%).
Main locations of FRATCH Experts, who have recently used OpenAI
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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Hamburg 
Munich 
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Frankfurt 
Stuttgart 
Dusseldorf 
Dortmund 
Essen 
Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
