OpenAI Experts in Germany

• Building a scalable nearshore IT developer hub (Croatia, Czech Republic, Poland) as an independent company using a BOT model (Build – Operate – Transfer)
• Identifying, selecting, and managing full-service agencies; introducing governance and control mechanisms including KPIs, SLAs, and regular service reviews
• Creating and reviewing data processing agreements and framework contracts in coordination with Legal & Compliance; integrating regulatory requirements (including KRITIS) into process design
• Advising on cloud-vs.-on-premise strategies, data storage, and authorization concepts; supporting procurement with tendering and vendor evaluations
• Change management and process harmonization between internal teams and nearshore partners; reporting to management board, CFO, and CIO

Result: Scalable IT developer hub with an audit-proof governance model, lower operating costs, and faster product development.

• 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

• A 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 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, verifiable data instead of plain text: requirements, use cases, and architecture decisions as a continuously linked knowledge graph, traceable from the requirement to the architecture decision – queryable for both people and AI agents alike. Technically based 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 licensing model). Requirements engineering and ubiquitous language hexagon active. Publicly available since 07/2026 as a Community Edition under Apache-2.0 (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

• Development of a browser-based platform for no-code deployment and cluster management in Kubernetes
• Implementation of a CLI tool in TypeScript to provision resources directly from the browser interface into the cluster
• Development of a expression parser in Go and delivery as a microservice to extract Helm expressions from values files
• Use of LLMs to turn user intent into Kubernetes diagrams
• Technology stack: Java, Go, OpenAI API, React, Azure, Next.js, Strapi, Stripe Connect

• 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

• 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

• Deployment and scaling of existing ML initiatives, including demand and cash flow forecasts.
• Building robust monitoring with mlflow for data stability, model performance, and drift detection, as well as implementing additional ML use cases.
• Further development of an Agentic AI chatbot for transparent and easy-to-understand model explanations.

• 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

• Training the AI models, evaluating images and texts for UI/UX, turning the provided data into insights via OpenAI Feather as part of the machine learning workflow

Technologies: OpenAI Feather

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

• Focus: system architecture, cloud-native platforms, microservices, API design
• Product: Pflege-Pfad is a digital matchmaking platform that connects relatives of people in need of care directly with verified care services and caregivers - without an agency and without ongoing fees.
• Business analysis & process design:
• Analysis of the German care market and identification of the key pain points of both target groups.
• Modeling of the core business processes: registration, verification, care request, application, placement, and rating.
• Definition of the business model as a freemium/premium model with optional contact unlocking.
• Creation of user stories and requirements documentation for relatives, care services, and administrators.
• Design of trust and quality assurance mechanisms with document upload, admin review process, and rating system.
• Coordination with stakeholders and validation of product decisions with potential users.
• Technical implementation:
• Design and implementation of the entire platform architecture as a solo developer.
• Design and implementation of a REST API with Spring Boot and Kotlin, including JWT-based authentication.
• Development of the frontend as a single-page application with Angular 17.
• Implementation of the AWS infrastructure with EC2, RDS PostgreSQL, S3, CloudFront, and IAM.
• Document upload with AWS S3 via presigned URLs for verification of care services.
• Email notifications via Resend API.
• AI-supported care service search via OpenAI API.
• Implementation of complete user flows such as registration, login, password reset, and placement process.
• Building an admin panel for user and care service management as well as analytics.
• CI/CD with GitHub Actions and containerized deployments with Docker.
• End-to-end tests with Playwright.

Technologies: Kotlin, Spring Boot 3, Spring Security, JWT, JPA/Hibernate, PostgreSQL, Angular 17, TypeScript, RxJS, AWS (EC2, ECS, S3, CloudFront CDN, RDS PostgreSQL, IAM), nginx, GitHub Actions, Playwright, Maven, Git, OpenAI API, Resend API, Docker, Scrum, i18n (DE/EN/TR), Kiro, feature-flag architecture.

Tools: Fabric, AWS, dbt, Power BI, SQL, DWH, R, Python

• Supported customers in implementing an architecture design for extracting and preparing data
• Planned the design and implementation of the BI and DWH platform
• Ensured the scalability and performance of the data platform

• Designed and implemented comprehensive business processes, leading cross-functional teams to increase customer satisfaction and reduce costs
• Provided training and ensured benefits realization through end-to-end workflow development
• Contributed to the “Generate Insights from Hidden Knowledge” initiative by developing and deploying AI-driven workflow automation solutions using Large Language Models (LLMs) and low-code/no-code platforms
• Designed multi-agentic workflows integrating OpenAI, LangChain, Haystack, and n8n to automate document review, data extraction, and knowledge summarization processes
• Led the orchestration of AI and automation frameworks to enhance medical and business review processes, ensuring compliance, explainability, and transparency
• Collaborated cross-functionally to translate complex business requirements into AI-enabled automation prototypes aligned with enterprise compliance and data privacy standards
• Applied Power Automate, UiPath, Nintex, and ServiceNow to deliver rapid, scalable, and secure automation solutions within validated operational environments
• Leveraged Lean Six Sigma, Agile/SAFe, and ITIL principles to structure AI development pipelines ensuring measurable impact, auditability, and sustainable governance
• Managed cross-departmental collaboration to standardize workflows, reducing errors and enhancing task management. Established governance frameworks to ensure the sustainability of automation solutions

• Conceptual design and implementation of modular AI assistants for sales and business processes in the Microsoft ecosystem (Agentic AI, Copilot extensions)
• Frontend architecture and development with React + TypeScript for embedded chat and assistant surfaces (streaming UI, hooks, React Query, OpenAPI clients)
• Enterprise-level agent development: reusable skill/agent library, MCP server, review and compliance gates
• LLM integration into the user experience: Anthropic (Claude), OpenAI, tool use, RAG pipelines, prompt engineering, guardrails
• Architecture and code review consulting as well as mentoring in the AI development team
• Integration with Microsoft Graph, Power Platform, and Azure services
• Technologies: React, TypeScript, Anthropic Claude, OpenAI, MCP, RAG, Microsoft Graph, Power Platform, Azure