Semantic Search Experts in Germany

Remote freelance role focused on Agentic AI strategy, secure application patterns, and reusable agentic workflows for a government agency.

• Development and implementation of an open source Agentic AI strategy for a government agency, with a focus on GDPR, security, and self hosted solutions
• Development of reusable agentic workflows and mini applications that enable non technical employees to solve business problems independently
• Implementation of internal business applications with Single Sign On (SSO) and Azure PostgreSQL integration on Hetzner Linux servers
• Implementation of nine mini applications with Single Sign On (SSO) and Azure PostgreSQL integration on Hetzner Linux servers
• Techstack: Python, Nextjs, Typescript, Streamlit, Anthropic SDK (Claude), Azure, Linux Ubuntu, PostgreSQL, MS SQL, Angular, Authentik

• Relaunched a self-developed semantic name recommendation product by combining semantic search, graph-based similarity analysis, LLM-/RAG-supported content, and AI-assisted development processes.
• End-to-end responsibility across the product lifecycle—from use case definition and solution design through prototyping and evaluation to the iterative development of the roadmap.
• Evaluated 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.

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 context-based business information.

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

Building 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, and 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 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.

• Complete greenfield rebuild across the whole stack — built AI-native: backend in Go and NestJS, PostgreSQL (CNPG) on K3s with GitOps/Terraform; frontend on Angular 22, zoneless.
• Orchestrated coding agents (e.g. Claude Code, Cursor) across the entire lifecycle — architecture, implementation, testing, reviews, documentation — driven by Specification-Driven Development (SDD).
• “Bruno” — LLM commentator persona backed by RAG and MCP for a personality that stays consistent across all generations (match previews, post-match reports, his own virtual bets).

Angular 22 (zoneless, without Zone.js), Claude Code, Claude Code Skills, CNPG, Cursor, Design Tokens (Spec for Code), Docker, Gherkin, Git, GitLab, GitOps, Go, Google Gemini, Grafana, Hetzner Cloud, K3s, Keycloak, Kubernetes, Lighthouse, LLM Integration, Model Context Protocol (MCP), NestJS, Node.js, NPM, Playwright, PostgreSQL, Prometheus, RAG, REST, Specification-Driven Development (SDD), Structured Outputs, Terraform, TypeScript, Vitest

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.

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

• 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.

• 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.