OpenAI Experts in Berlin
matched in minutes by AIHire experts who design GPT-powered applications, connect language models to business data, and deliver reliable automation for customer service, research, and internal workflows. Find vetted, available freelancers precisely matched to your needs.
Meet FRATCH Experts in Berlin, who have recently used OpenAI
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
Stefan O.
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.
Abhishek N.
Last position:
Fullstack Developer at DAMALO GmbH
- Own full-stack development of an AI-native enterprise platform built on TypeScript, React, Vite, tRPC, Hono, and PostgreSQL, delivering AI-powered consulting workflows to B2B clients.
- Designed and shipped a multi-agent AI system using ReAct framework and Claude skills-style workflow patterns, including an intelligent PM assistant with rich system prompts, slash commands, tool integrations, and streaming chat UI.
- Architected an LLM evaluation framework: rubric-based LLM-as-judge, golden datasets, regression testing, and automated quality gating — ensuring consistent AI output quality at scale.
- Integrated LangFuse for end-to-end LLM tracing, conversation replays, and evaluation pipelines, enabling data-driven prompt optimisation that reduced token costs and response variance.
- Built with Drizzle ORM, pgvector, and knowledge graphs for structured data access, semantic search, and relationship-aware AI reasoning across the platform.
- Led TanStack React Query migration across the application — replacing manual state management with centralised caching and automatic refetching, reducing data-fetching boilerplate significantly.
- Practiced AI-native development throughout: Claude Code, Codex, Perplexity SDK, and LLM-assisted testing across the full development lifecycle. Deployed on Vercel + Azure ACA with Biome for linting/formatting.
Aruldass A.
Last position:
Web Module Lead at Mphasis Limited
- Led the end-to-end delivery of enterprise full-stack web applications by driving requirement analysis, solution design, frontend and backend development, database design, API integration, code reviews, team coordination, Agile execution, CI/CD deployments, production support, performance optimization, security implementation, and stakeholder collaboration to deliver scalable, high-quality software solutions.
Jorge N.
Last position:
Senior Developer at SafeXSmart KI Solutions UG
AI Platform Backend – Senior Developer
Brought in to design and build a backend for an AI platform from scratch, including multi-provider LLM orchestration and real-time infrastructure for AI influencer personas at scale.
Tasks and responsibilities
- Architecture and implementation of a multi-LLM orchestration layer with Semantic Kernel to integrate GPT-4 and other providers for core platform logic and AI influencer personas, reducing model-switching overhead by abstracting provider APIs behind a single interface.
- Design and development of a backend from scratch in C# / .NET 10, including domain modeling with DDD, a versioned RESTful API layer, and cloud infrastructure setup on Azure.
- Built a real-time chat infrastructure with Server-Sent Events (SSE), message persistence, and delivery guarantees for live operation of AI influencer personas at scale.
- Developed a media management service with integration of cloud object storage for upload and retrieval of influencer-generated content.
- Created an integration and unit test suite with data seeding for reliable regression testing across all core platform flows, significantly reducing production error rates.
Tools and technologies: C#, .NET, ASP.NET Core, Python, TypeScript, MySQL, Semantic Kernel, EF Core, Minimal APIs, LLM Orchestration, Prompt Engineering, Agentic AI, Generative AI, AI-Assisted Engineering, Claude Code, GitHub Copilot, Google Gemini, OpenAI API, Ollama, Redis, Azure, Azure Container Apps, Azure Database for MySQL, Docker, GitHub Actions, Clean Architecture, Vertical Slice Architecture, CQRS, Domain-Driven Design, REST API, xUnit, Integration Testing, Unit Testing, Jira, Confluence, Scrum
Murad H.
Last position:
Founder & Technical Lead at Hubpoint.Ai
- Founded an AI-powered scheduling and business-management SaaS for SMBs, owning technology strategy, architecture, product development, UX, billing and go-to-market execution.
- Architected and shipped a multi-tenant platform with REST APIs, RBAC, CRM, billing and notifications, powering the manager dashboard, admin console, booking experience and iOS/Android applications.
- Led and mentored 7 software engineers, 1 DevOps engineer, 1 QA engineer and 1 UX/UI designer, while remaining hands-on across backend, frontend and product delivery.
- Built AI voice and chat agents using Python/FastAPI, OpenAI and Anthropic APIs, RAG, pgvector and tool calling; integrated Twilio, Google Calendar/Meet, Stripe and Firebase.
- Owned production infrastructure and automated delivery across separate environments using Docker, Nginx, GitHub Actions and Grafana; represented the company at accelerators and international startup events.
Selected stack: Python, FastAPI, Node.js, Vue 3, React/Next.js, React Native, PostgreSQL, Redis, Docker
Sunish B.
Last position:
AtlasMind - Production AI assistant for Jira at Mercedes Benz Innovation Labs Gmbh
- Converts natural language into JQL using RAG and pgvector. Returns structured JSON with a query, chart spec, and plain-text answer. A two-stage router answers general questions without touching the JQL pipeline at all.
- Interchangeable LLM backends: Ollama, vLLM, Groq, Anthropic Claude, AWS Bedrock - switchable at runtime, no code changes. Self-healing JQL: on Jira validation failure, feeds error back to LLM, retries up to 4 times. OCI Vault for secrets. Deployed on Oracle Cloud A1 with GPU inference over Tailscale private network. Open source.
Muzamal A.
Last position:
Data Scientist / AI Consultant at HelmX
- Delivered AI and data science solutions, including LLM-based chatbots and data pipelines, improving operational efficiency.
- Collaborated on product features, achieving measurable impact and maintaining strong client relationships.
Hamza K.
Last position:
Academic Research Contributor in Health Sector (Volunteer)
- Acted as technical consultant to optimize multi-layer ensemble models combining ResNet, CNN-BiGRU-Attention, and XGBoost.
- Guided implementation of a Logistic Regression meta-learner to solve class imbalance problems, achieving 92.86% accuracy and 0.9644 AUC on PTB-XL and Chapman-Shaoxing datasets.
Nune I.
Last position:
Fractional CTO at OpsWorker
OpsWorker turns Kubernetes alerts into root-cause analyses, on top of the monitoring a team already runs. I lead the technical side: the agent architecture, the AWS infrastructure it runs on (fully inside EU regions), and the engineering decisions behind it, read-only in the cluster by default, human in the loop for judgment. The stack underneath: Amazon Bedrock and Bedrock AgentCore, agents built with the Strands Agents SDK, the Claude and OpenAI APIs, and the Kubernetes API.
Enrico G.
Last position:
Freelance Software & Data/AI Engineer at Freiberuflicher Software & Data/AI Engineer
- Lecturer for the GenAI Track at the Master School Institute of Technology
- Development of a full-stack AI application (React + Python/FastAPI) for automated supplier product import with intelligent column and category classification (4-layer hierarchical) including human-in-the-loop validation
Ibrahim H.
Last position:
Senior Full Stack / AI Engineer at Punktum Digital GmbH
- Context: Healthcare and laboratory teams required faster document analysis, treatment-planning support, and reliable AI workflows for MR/VR-assisted operations.
- Contribution: Built the AI healthcare platform, model/agent workflows, VR-glasses deployment platform, REST APIs, Next.js/React interfaces, and CI/CD pipelines.
- Impact: Delivered a production-ready AI product foundation that improved clinical document review, supported laboratory automation, and made VR fleet deployment manageable across environments.
Tech: TypeScript, Next.js, Node.js, React, Java, Spring Boot, Python, PyTorch, TensorFlow, Docker, PostgreSQL, OpenAPI, GitLab, GitHub Actions.
Erik W.
Last position:
AI Workflow and Process Automation at Self-employed
- Process analysis and target concept: discussions with responsible stakeholders and users, system and handover model, bottleneck analysis and acceptance criteria.
- Defined automation modules, built with AI support using Python/FastAPI, TypeScript/Next.js, SQL/PostgreSQL, Supabase, REST APIs and Webhooks; I am responsible for the specification, acceptance criteria and acceptance testing.
- Document and decision workflows from intake, research and extraction through to decision documents, CRM updates or controlled system actions.
- Quality assurance and handover with test cases, logging, exception paths, operational documentation and knowledge transfer.
Ersin K.
Last position:
Founder & Lead Architect at ORBYNT / 7Style
- Full automation of the software development process: ticket analysis → AI coding agents → pull request → automated code review → deployment
- Multi-tenant architecture with 82 database models and real-time WebSocket monitoring
- Integration of 40+ AI tools with Claude & GPT
- Tech stack: React, TypeScript, Express.js, PostgreSQL, Redis, BullMQ
- Platform in productive use with paying customers
André B.
Last position:
External Attack Surface Assessment & Cybersecurity Readiness Checks at Graydaxe Cybersecurity GmbH
- Conducting cybersecurity readiness checks based on an in-house assessment methodology
- Analyzing the external attack surface using the Graydaxe EASM platform
- Assessing maturity levels and deriving prioritized recommendations for action
Discover over 15,000 top freelancers
Statistics of experts using OpenAI
Aggregated from the professional profiles of matched freelancers.
Experience
14 years (Germany: 16 years)
Position duration
1.8 years (Germany: 2.8 years)
Positions per freelancer
9 (Germany: 11)
Top business areas
Product Development, Information Technology, Research and Development
Top industries
Information Technology, Professional Services, Banking and Finance
Certification focus areas
Information Technology, Product Development, Project Management
Bachelor's degree or higher
96% (Germany: 90%)
Master's degree or higher
55% (Germany: 62%)
Certifications per freelancer
3
Most common languages
English, German, Spanish
Speak two or more languages
93% (Germany: 98%)
Based on our profile pool as of 19 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Berlin 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.
Discover detailed OpenAI rate benchmarks:
Explore rate insightsAverage rates of experts in Berlin 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 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
OpenAI 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 (98%)
- Professional Services (49%)
- Banking and Finance (45%)
- Healthcare (40%)
- Retail (35%)
- Automotive (33%)
- Education (31%)
- Manufacturing (27%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What OpenAI provides
OpenAI develops artificial intelligence models and tools for working with language, images, audio, and structured data. Companies use the OpenAI API and ChatGPT to add conversational interfaces, content generation, document analysis, extraction, classification, and workflow automation to their products. The right implementation connects these capabilities to a clear business process rather than treating a model as a standalone feature.
Models and APIs
OpenAI projects can involve GPT models, embeddings, structured outputs, image generation, speech-to-text, and text-to-speech. Specialists work with API authentication, prompt design, tool calling, streaming responses, context handling, and usage controls. They also select suitable model behavior, response formats, and safeguards for each task.
Typical applications
- Customer support assistants connected to approved knowledge sources
- Search and question answering across contracts, manuals, or reports
- Structured extraction from invoices, applications, and business documents
- Content workflows with review steps and brand controls
- Voice, image, and text features inside digital products
Engineering around models
Useful OpenAI solutions depend on the surrounding system. Professionals often combine the API with Python or TypeScript services, REST and event-driven integrations, databases, vector search, retrieval-augmented generation, and cloud infrastructure. They may also build evaluation sets, prompt versioning, access controls, logging, caching, and human approval flows.
When companies need specialists
Companies bring in freelance OpenAI expertise when a proof of concept must become a dependable product, an existing assistant gives inconsistent answers, or sensitive business information needs a controlled retrieval flow. Berlin teams may benefit from on-site workshops or remote collaboration across Germany, depending on their delivery process and language needs. A specialist can also review vendor choices, data flows, and production readiness.
What strong professionals deliver
Strong OpenAI professionals define success criteria before tuning prompts. They test representative inputs, measure factuality and refusal behavior, protect personal and confidential data, and make failure paths visible to users. They explain model limitations clearly, keep application logic separate from generated text, and deliver maintainable integrations that can evolve as models and requirements change.
Frequently asked questions
The facts hiring teams ask for most often when it comes to OpenAI.
Companies use OpenAI to add conversational support, document understanding, content assistance, semantic search, data extraction, and voice or image features to software. The API can support both customer-facing products and internal workflows when it is connected to trusted data and clear review rules.
OpenAI offers managed models and APIs, while open-source models can provide more control over hosting, customization, and data location. The better choice depends on response quality, integration effort, security requirements, operating constraints, and the level of infrastructure ownership a company wants.
A strong OpenAI specialist often also understands backend development, API integration, databases, vector search, retrieval-augmented generation, cloud deployment, and information security. Experience with evaluation, observability, prompt versioning, and user experience is equally useful for production systems.
The required depth depends on the scope. A simple prototype may need focused API and prompt expertise, while a production system calls for OpenAI experience alongside testing, access control, data handling, monitoring, and failure recovery. The professional should show work comparable to the risk and complexity of the project.
Yes. OpenAI projects are usually suitable for remote delivery because requirements, prompts, test cases, API changes, and evaluation results can be shared digitally. Berlin companies should agree on working hours, workshop expectations, documentation, and whether German-language communication is needed.
Before using OpenAI, the freelancer should clarify the user problem, approved data sources, privacy boundaries, expected response behavior, review steps, and success measures. They should also confirm the existing stack, deployment environment, access permissions, and who owns ongoing model and prompt changes.
Ask for representative evaluation cases, failure examples, monitoring plans, and an explanation of how inaccurate or unsafe responses are handled. High-quality OpenAI work is measurable, traceable, secure, and designed for graceful failure rather than judged only by a convincing demo.
OpenAI may be suitable when the solution uses an approved data flow, suitable access controls, careful retention decisions, and appropriate legal and security review. A specialist should map where information travels, limit retrieval to authorized content, avoid unnecessary exposure, and document the safeguards before production use.
The average hourly rate of freelancers in Berlin, Germany who have used OpenAI in their recent projects is 96 €, which corresponds to a daily rate of about 772 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used OpenAI in their recent projects, 96% hold at least a Bachelor's degree and 55% hold at least a Master's degree.
On average, freelancers in Berlin, Germany who have used OpenAI in their recent projects have 14 years of professional experience, with a single engagement typically lasting around 1.8 years.
The most common languages among freelancers in Berlin, Germany who have used OpenAI in their recent projects are English (98%), German (89%), and Spanish (15%).
The most common industries among freelancers in Berlin, Germany who have used OpenAI in their recent projects are Information Technology (98%), Professional Services (49%), and Banking and Finance (45%).
The most common business areas among freelancers in Berlin, Germany who have used OpenAI in their recent projects are Product Development (96%), Information Technology (95%), and Research and Development (60%).
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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Munich 
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Stuttgart 
Dusseldorf 
Dortmund 
Essen 
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FRATCH CEO
