Microservices Experts in Dortmund

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

Manufacturing intelligence platform with enterprise workflows, RAG, real-time AI assistant features, and multi-repository backend architecture.

• Built and extended production AI/backend services with Django, DRF, FastAPI, GraphQL, Celery, PostgreSQL, MySQL, Redis, and WebSockets across a modular multi-repository platform.
• Contributed to ARIS V2, a real-time manufacturing AI assistant using LangChain, LangGraph, MCP tool orchestration, planning/execution flows, OpenAI, AWS Bedrock, Qdrant, and Elasticsearch/OpenSearch-backed retrieval.
• Supported rollout expansion from ARIS V1 in 4 of 17 client production plants to ARIS V2 currently active in 13 of 17 plants, increasing real-world deployment coverage to more than 50% of the client footprint.
• Worked on document-grounded RAG functionality including ingestion, OCR, chunking, embeddings, indexing, retrieval, reranking, and grounded answer generation for industrial workflows.

Stack: Python, Django, DRF, FastAPI, LangChain, LangGraph, GraphQL, Celery, WebSockets, OpenAI, AWS Bedrock, Qdrant, Elasticsearch/OpenSearch, PostgreSQL, MySQL, Redis, Docker.

• 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

• Technical design, prioritization, and hands-on implementation of AI automation, conversational AI, voice AI, and private AI solutions.
• Translation of business requirements into system architecture, conversation/workflow logic, APIs, data flows, and production-ready AI workflows.
• Development of backend and integration components with Python/FastAPI, PostgreSQL/pgvector, Redis, REST APIs, webhooks, and background jobs.
• Design and implementation of RAG architectures with local/private LLMs, embeddings, document processing, semantic search, and controlled access to company knowledge.
• Prompt engineering for LLM and voice/conversational AI applications; iterative optimization based on real processes, system responses, and quality requirements.
• Practical work with ElevenLabs and its APIs in a voice AI project, as well as technical evaluation and integration of voice AI components.
• Deployment and operation of Docker-based systems on Linux with Nginx, CI/CD, monitoring/logging, backup/restore, and reproducible deployment processes.

Building an observability and alerting solution for Business Central Job Queue Monitoring

• Built a central monitoring solution to monitor the Business Central Job Queue in a multi-organization environment in Azure DevOps
• Integrated Azure Application Insights and Log Analytics for the structured collection and analysis of job runtimes and error events
• Developed KQL queries for targeted analysis of failed jobs and detection of error patterns
• Connected Grafana and Power BI to visualize operational metrics and job statuses in real time
• Implemented an automated alerting workflow with Power Automate – an email notification is triggered immediately when a job fails
• Ensured transparency into the status of productive background processes during ongoing operations

Result: Full transparency into failed Business Central jobs · Significant reduction in response time through automated email alerting · Stable foundation for the proactive operation of productive background processes

Technologies: Azure DevOps | Application Insights | Log Analytics | KQL | Grafana | Power Automate | Power BI | Business Central | Azure Monitor

Development and evolutionary further development of a highly available, cloud-native microservice and integration architecture for dealer and retail processes in the Porsche Car Configurator.

Responsibilities

• Development of Java-/Kotlin-based backend, API, and integration components (Spring Boot)
• Integration of internal and external systems via REST/OpenAPI, GraphQL, Apache Kafka, and AWS SQS (synchronous and asynchronous)
• Implementation of stable, high-performance communication and data flows in a cloud-native platform architecture
• Processing of structured data formats (JSON, Protobuf, GraphQL schemas) based on existing API patterns
• Performance optimization of distributed microservices with reduced response times and higher operational stability
• Technical tests (unit, integration, and API tests) as well as error analysis in production-like environments
• AWS Infrastructure as Code with Terraform and AWS CDK
• CI/CD automation (build, test, and deployment pipelines) with GitHub Actions
• AI-supported feature implementation (GitHub Copilot Agent)

Label: Kotlin, Java 25, Spring Boot 4, Protobuf, TypeScript, AWS, Terraform, CDK, Apache Kafka, AWS SQS, REST/OpenAPI, GraphQL, JSON, PostgreSQL, Docker, GitHub Actions, Maven, Gradle, JUnit, Mockito, Testcontainers

• Various small consultations, e.g. concepts and arcitectures.
• Updating my Matlab, Linux, IPv6, Cyber-Security, C++ (up to 23) and Python skills.