Semantic Search Experts in Germany

• Relaunch of a self-developed semantic name recommendation product by combining semantic search, graph-based similarity analysis, LLM-/RAG-supported content, and AI-supported development processes.
• End-to-end responsibility in the product lifecycle - from use case definition and solution design to prototyping and evaluation, and then iterative roadmap development.
• Assessment of AI use cases in terms of user value, technical feasibility, data quality, governance, and operating costs to guide MVP scope, roadmap decisions, and continuous product improvement.

Industry: Publishing, media Project management for the concept of a RAG-based archive access solution: a secure on-prem or hybrid compute architecture for LLM and embedding operations, pipeline for transcription and automatic tagging, semantic search across audio and video archives. Use case evaluation and make-or-buy together with editorial team, archive, and legal department, taking into account copyright, broadcasting law, and the AI Act. Differentiator: practical LLM infrastructure experience from two own productive platforms combined with C-level program management in regulated industries.

• 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

Conversational AI assistant for cloud infrastructure and security queries

• Designed FastAPI backend with multi-turn conversation handler, token budgeting, and context window management.
• Integrated Amazon Bedrock (Claude 3 Sonnet/Haiku); built RAG pipeline with MongoDB chat history and semantic search.
• Implemented Factory Pattern for modular LLM provider switching; reduced model onboarding effort by 60%.
• Reduced LLM inference cost by 35% through model tiering (Haiku vs Sonnet) and prompt/entity consolidation.

Tech: Python, FastAPI, Amazon Bedrock, MongoDB, Streamlit, Pydantic.

• 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.

Embedded Analytics & AI-assisted BI

Design and development of an integrated analytics solution based on ASP.NET Core, Power BI Embedded, and LLM services to provide contextual business information.

Development of an AI agent with Function/Tool Calling for secure orchestration of REST APIs, SQL data sources, and technical services within defined business processes.

Build-up of automated BI workflows including workspace management, deployment processes, and scheduled refresh via the Power BI REST API.

Implementation of secure service-to-service communication with Microsoft Entra ID and service principal, as well as integration into existing enterprise system landscapes.

Technologies: ASP.NET Core, C#/.NET, Power BI Embedded, Power BI REST API, LLM API, AI Agents, Function/Tool Calling, Entra ID

Designed and developed an AI-powered automation platform using n8n to analyze social media niches, identify target audiences, and automate marketing strategy generation. The solution combined AI agents, workflow orchestration, and data analysis to automate research processes and generate data-driven insights.

• Designed and implemented complex automation workflows using n8n
• Developed AI-powered analysis agents for market and audience research
• Integrated multiple APIs and AI services into automated workflows
• Built automated market, competitor, and target audience analysis pipelines
• Leveraged Large Language Models (LLMs) for information summarization, classification, and prioritization
• Containerized and deployed the platform using Docker

Technologies: n8n, AI Agents, OpenAI APIs, Prompt Engineering, LLMs, Docker, Linux, REST APIs, Webhooks

• 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.

• Concept and implementation of data-heavy platforms for enterprise customers
• Design of complex AI-based interactions (chatbots, voice control, semantic search)
• Stakeholder management with more than 10 participants in cross-functional teams
• User research: interviews, usability tests, value validation
• Definition of quality metrics and execution of value analyses

Tools & methods: Figma, Adobe XD, Miro, Chat GPT, Claude | Scrum, Kanban, SAFe, Lean UX

Selected clients: VW Group, Volkswagen, BMW, Cariad, ABUS, Gieseke & Devrient, Hamburg Senate Chancellery, German Red Cross, Deutsche Bahn, Immoscout24, Relynk

• Designed and deployed enterprise Retrieval-Augmented Generation (RAG) applications using LangChain, LangGraph, vector databases, embeddings, and open-source LLMs served through vLLM on GCP GPU infrastructure.
• Built agentic AI workflows using LangGraph with planning, reasoning, tool execution, persistent memory, session management, and Human-in-the-Loop approval mechanisms.
• Developed LLM-powered automation systems integrating BigQuery, SQL pipelines, and external advertising APIs including Meta, TikTok, Amazon, Snapchat, Google, and Pinterest, reducing manual operational workflows.
• Architected multi-agent AI systems for enterprise analytics and decision-support workflows, enabling autonomous task execution and intelligent data interactions.
• Implemented retrieval optimization strategies including multi-retriever architectures, semantic search, context optimization, and query improvement techniques, improving response relevance by approximately 40%.
• Engineered structured prompting strategies, function-calling schemas, and validation workflows to improve reliability of multi-step LLM applications.
• Designed scalable AI services using Python, FastAPI, Cloud Run, Pub/Sub, BigQuery, Docker, and cloud-native deployment architectures.

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

• Architected and implemented 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.
• Designed and developed 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 the production error rate.

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

• Building an AI & Data Consultancy Practice with the goal of helping European companies adopt Artificial Intelligence and modern data platforms
• End-to-end further development of a production system using modified coding agents (OpenCode). Tech stack: Kubernetes, Argo, Keycloak, Typescript, Grafana, GitOps, DevOps, Playwright
• Internal research project on the use of coding agents in the field of mathematical logic for creating formal models. Use of Cursor IDE and Codex, Codex CLI. Architecture design, quality control and refactoring, as well as writing code and tests. Repository (open source) available pre-launch
• Research on the role of mathematical logic as a formal language that connects IT and AI with business processes
• Project lead for collecting and deploying parking recommendations for rail vehicles with significant savings potential based on real-time data in a mobility and transport company
• Project lead for collecting and distributing process measurement points for real-time control in a mobility and transport company
• Deputy application owner for an app used for communication in the dispatching and provision of rail vehicles

• ▶ Built real-time voice assistant (STT → LLM → TTS pipeline) benchmarking and evaluating multiple STT providers including faster-whisper and Azure Speech. achieved sub-3s latency, Groq API (Llama 3) with multi-turn memory - directly handling edge cases in dictation, names and passcode recognition.

Re-platforming an e-commerce shop to a microservice architecture

• Goal: Replace an outdated Shopware system with a scalable, future-proof solution based on microservices and a headless architecture.
• Led a full architecture consulting process and defined the microservice boundaries based on a headless architecture with commercetools as PIM/OMS and Next.js as the frontend solution.
• Developed and integrated several decentralized services (e.g. internationalization, personalization).
• Took over the configuration of central third-party systems such as Contentstack and Algolia.
• Built a stable cloud infrastructure on Google Cloud with monitoring via Grafana.

Technologies: commercetools, Next.js, Contentstack, Algolia, Google Cloud, Grafana, TypeScript, Shopware

Development of an international e-commerce platform

• Goal: Build a high-performance, user-friendly and international e-commerce platform.
• Defined a scalable, high-performance and maintainable software architecture that served as the foundation for the platform's international expansion.
• Selected a best-of-breed technology stack that enabled the development of an industry-leading shop and reduced development effort for new features by 30%.
• Ensured seamless integration of critical third-party systems (PIM, CRM, ERP) to guarantee end-to-end business processes and a consistent data foundation.
• Implemented comprehensive tracking and analytics tools for continuous performance monitoring and optimization of the customer journey.

Technologies: React.js, Next.js, commerceTools, Algolia, Salesforce, Heroku, CI/CD, PHP, Google Analytics

AI-powered personalization and customer data platform in e-commerce

• Goal: Replace static content with a dynamic, AI-based personalization strategy to increase user relevance and automate marketing processes.
• Designed and built a customer data platform to aggregate and combine customer and analytics data from distributed sources.
• Implemented automated categorization of customer profiles as the basis for delivering personalized content and product recommendations in the Shopware frontend.
• Developed a semantic similarity algorithm based on Python and OpenAI to calculate product and content similarity from user profiles.
• Built the technical connection to retail media platforms to control external ad placements along the customer journey.

Technologies: Shopware 6, Python 3, OpenAI, Elasticsearch, PHP, Symfony, Twig

Shopware tracking & consent architecture (GDPR) for 4 online shops

• Goal: Build a unified, GDPR-compliant tracking infrastructure across multiple shops with central consent management across several Shopware instances.
• Defined a comprehensive tracking guide and developed a modular architecture compatible across multiple Shopware versions.
• GDPR-compliant integration of Usercentrics and Adobe Launch through a central tag manager.
• Full tracking setup (page, order, product, user) incl. partner-specific tracking (Emarsys, Channelpilot, etc.).
• Detailed event and error tracking to proactively identify technical drop-offs.

Technologies: Shopware, Adobe Analytics, Usercentrics, Tag Manager, PHP, MySQL, GDPR

AI/LLM search engine with RAG and hybrid search (Python, Elasticsearch)

• Goal: Build an AI-powered search engine with RAG architecture and hybrid search to accurately match service providers from over 500,000 company records.
• Developed an automated data pipeline (web scraping + LLM) that continuously crawls company data and converts it into structured formats using LLMs.
• Implemented a RAG workflow incl. vectorization for semantic search to increase search accuracy and relevance.
• Configured and fine-tuned Elasticsearch for hybrid search (vector + keyword search).
• End-to-end development of backend API, frontend and deployment on live servers.

Technologies: Python, FastAPI, Elasticsearch, LLM, RAG, React, Docker, Web Scraping

AI/computer vision system (Python, ML) – object detection under difficult conditions

• Goal: Develop an AI-powered recognition system with reliable performance even in rain, fog, snow and darkness.
• Built and annotated a large training dataset incl. difficult conditions.
• Trained a YOLO-based object detection model; carried out systematic error analysis and improved data quality and preprocessing.
• Coordinated with stakeholders through regular status updates.

Technologies: Python, Machine Learning, TensorFlow, PyTorch, YOLO, OpenCV