Langfuse Experts in Germany

• This project involved extraction of intelligence data to support report writing for a company that provides geopolitical, global, commercial intelligence. The data have been gathered from a number of resources (interview transcripts, online data, internal documents), and then a knowledge base has been build from it. This was the basis of a complex RAG system, that was evaluated against a golden dataset. Agents have been used to find out the contradicting intelligence, the statements supporting each other, and to store back the generated knowledge.

Used: Python, RAG, LangGraph, LangChain, deepeval, MCP

• AI Integration (LLM & RAG): Designed and built an internal intelligent RAG system (Retrieval-Augmented Generation) based on LLMs, n8n, and vector data to automatically analyze technical documents and error logs.
• Design & Implementation: Designed a robust RS-232/UART communication interface for an SBC-based embedded device used to control medical shaker systems.
• Architecture & Protocol Design: Built a highly maintainable software structure (OOP, SOLID) and defined resilient, hardware-level communication protocols, including multithreading and advanced error handling.
• Quality Assurance & DevOps: Automated testing with xUnit, ran integration tests directly on the hardware target, and maintained technical documentation to strict medical technology standards via Azure DevOps.

Label: C#, .NET, LLMs, RAG, n8n, RS-232, UART, Multithreading, async/await, xUnit, gRPC/protobuf, Blazor, MudBlazor, EF Core, Visual Studio 2026, Azure DevOps

• Shipped Recare Voice Desktop from 0 to 1 in two months, including multi-language clinical documentation that auto-transcribes into structured German medical notes.
• Reduced LLM inference costs by 60–70% across Docs and Extract through prompt caching architecture.
• Built the AI workbench used by PMs/engineers for prompt experimentation and the Langfuse eval stack (10k+ traces evaluated).

Enterprise AI content categorization and AI-powered web research.

• Built multi-LLM evaluation framework with annotated data
• Iterated LLM error rates based on annotated datasets
• Implemented AI-powered web research pipeline Stack: LLM, evals, OpenRouter, Python, Node.js, TypeScript, React

Task:

• Design and build a proof of concept (PoC) for a future-proof virtualization platform, taking secure system architectures into account
• Assess the current state of existing infrastructures and develop selection and evaluation criteria for the right OS virtualization platform
• Carry out the requirements analysis and then create and prioritize tickets in the ticket system
• Complete and continuously update a tool evaluation matrix based on PoC results
• Support team knowledge building through clear documentation of the approach and results in Confluence
• Enterprise analysis of existing hardware (creating different BoMs)

Technologies: Vmware, Vmware Aria Operations, Osism, Canonical OpenStack, FishOs, Linux, Terraform, Ansible, Confluence, Alma

• Hired to build the People function from the ground up as the first People hire
• Defined the hiring plan and closed 2 Sales Manager hires
• Ran a people and culture audit and built the 12-month People roadmap
• Managed the hiring freeze and overall communication after the company lost its DiGA listing following a regulatory decision

Independent engineering practice focused on custom AI systems, production delivery, and fractional technical leadership.

Selected client engagement: Scalutions

• Role: Serve as fractional CTO and hands-on technical lead, responsible for the architecture and agentic infrastructure behind its managed B2B outbound operation.

• Product: Designed and built OutboundLoop, an agentic SDR operating system for research, qualification, personalized outreach, campaign management, human approvals, measurement, and continuous improvement.

• Scope: Own the full system lifecycle—from business processes and agent behavior to context design, model routing, integrations, evaluation, telemetry, reliability, cost control, and production operations.

• 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.

• 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.

End-to-end responsibility for all products – conception, architecture, development, and production operations as the sole developer; also customer discussions, proposals, and marketing.

Underwriting Copilot – AI assistant for industrial insurance (in production with a customer)

• Supports underwriters in analyzing industrial insurance submissions – in production at an industrial insurer.
• Framework-independent RAG architecture with Hybrid Search (BM25 + pgvector) across large, heterogeneous document collections.
• Two-stage evaluation and observability pipeline (code assertions + LLM-as-Judge) that makes answer quality, retrieval accuracy, and citation integrity measurable in a regression-safe way.

Kaiki Menu Analyzer – Data intelligence platform (in production with a customer)

• Automatically collects and analyzes food data from around 25,000 German restaurants.
• Scalable 7-container architecture (FastAPI, partitioned PostgreSQL, Redis/RQ) with LLM-supported extraction of structured data from PDF, HTML, and images.
• Complete CI/CD pipelines (GitHub Actions), production cloud deployment, interactive dashboards (Dash).

Kaiki GEO Atlas – GEO platform (in production with a customer)

• Measures brand visibility across five AI engines (ChatGPT, Gemini, Perplexity, Grok, Claude), each augmented with web search, orchestrated as a DAG workflow pipeline (Dispatcher → Sub-Workflows → Scoring → Report) with fail isolation.
• 6-container deployment (FastAPI, Celery, Redis, PostgreSQL); LLM cost estimation, PDF audit report, rule-based cross-signal insights (no additional LLM effort).

Data Pipeline & Analytics Platform – Competitive analysis in the automotive aftermarket

• Automated data pipeline with gap analysis algorithms and role-based access control; 230+ tests.
• Backend with FastAPI, PostgreSQL, SQLAlchemy.

Product development (actively in development)

BankingGPT – AI assistant for complaint management in cooperative banking

• Security architecture at its core: no AI draft reaches the customer without human approval – the approval decision is stored in auditable code, not in the language model (monotonic: the model may increase the level, but never lower it).
• Genuine agentic components, each with its own boundaries: the model selects tools itself through an MCP server (read-only, allowlisted, capped, fail-safe); sensitive cases are sent via the open A2A protocol (JSON-RPC, Agent Card, message/send/tasks/get; client implemented independently) to a standalone specialist agent (securities/law), which never lowers the review requirement (pinned by tests).
• Evaluation-driven across ten analysis rounds; an independent review and blind tests uncovered a security defect that nine automated runs had missed.
• Voice AI frontend, responding live: covered cases are answered during the conversation, while sensitive cases are escalated before generation; measured response latency < 7 s (local GPU STT/TTS).

Stack & production readiness: Python, pydantic-ai, FastAPI/Celery, PostgreSQL/pgvector, FastMCP, fasta2a, Docker; multi-tenant (physical vector isolation per tenant), encrypted PII, OWASP-LLM-reviewed, 275 tests, CI/CD; provider-portable (Ollama / EU-Cloud-Vertex).

After-Sales Assistant – agentic RAG/GraphRAG assistant based on public OEM manuals (automotive after-sales)

• Genuinely agentic with LangGraph: ReAct agent with four tools and conversation memory – the model decides itself whether to use the manual (RAG, Chroma), a knowledge graph (GraphRAG, Neo4j/Cypher – decoding warning lights), or a workshop/booking service.
• Human-in-the-loop before the irreversible action: before every appointment booking, the graph pauses (interrupt) and obtains the driver's explicit confirmation – the same approval-before-action discipline as in BankingGPT, in a different framework.
• Eval as a CI gate: three-part scorecard (RAGAS grounding + deterministic tool-routing accuracy + DeepEval safety: does the answer mention the warning first in the case of a critical warning?) blocks the pipeline; provider-agnostic (OpenAI/Azure/Anthropic), FastAPI with token streaming.

Stack: Python, LangChain/LangGraph, Chroma, Neo4j, RAGAS/DeepEval, FastAPI, Docker.

• Lead a cross-functional squad of backend, frontend, and ML/data engineers, balancing hands-on contribution (architecture, coding, reviews) with team leadership (mentoring, backlog prioritization, roadmap alignment).
• Designed and delivered ML-powered search and discovery features, including Learning-to-Rank (LTR), query expansion, and vector search, improving result relevance and user engagement.
• Implemented personalization and recommendation pipelines, using behavioral data and segmentation to increase customer retention and lifetime value.
• Established data-driven practices, building A/B testing and experimentation workflows (Odyn, MLflow) to measure feature impact on CTR, NDCG, and conversion.
• Owned the squad’s architecture and delivery roadmap, modernizing services with cloud-native microservices and event-driven systems (AWS, Pulumi, Terraform) to improve scalability and reliability.
• Improved reliability and operational excellence, introducing observability (Prometheus, Grafana, NewRelic), incident management, and postmortems that reduced downtime for customer-facing services.
• Mentored and supported engineers, fostering technical growth, collaboration, and a customer-first mindset through regular feedback, coaching, and code reviews.
• Worked closely with product managers, researchers, and business stakeholders to translate customer insights into technical solutions that improved discovery, engagement, and retention.
• Explored Generative AI/LLM use cases (GPT-4, LangChain, RAG), prototyping intelligent assistants and personalized discovery workflows that increased user satisfaction.
• Delivered tangible results: boosted engagement through personalization, contributed to revenue uplift, and reduced incidents by embedding resilience and observability.

• 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.

• Industry: B2B
• Headless frontend with AEM integration
• Key challenge: Migrating a PWA application in live operation based on .NET and legacy code; the entire application must be converted to React and Express.js/TypeScript
• Technical frameworks: Tailwind, XML, JavaScript, Caddy, ReactJS, Express.js, REST API, JSON
• Cloudflare CDN
• Caddy server with GitHub CI/CD pipeline

• Implementation of new features in the Admin Backend

• Automated tests (Unit, E2E)

• Architecture coordination with external partners

• Functionalities: user management, room administration, release automation.