SQL 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

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

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.

• DTO class generator: In-house development of a tool for the automatic generation of classes, functions, and methods for use in .NET Core projects based on SAP Business One Service Layer metadata
• E-invoice import add-on: Development of a SAP Business One add-on / coresuite module for the automated import of ZUGFeRD e-invoices as incoming invoices – with configurable mapping to flexibly control the import logic across four mapping types (DIRECT, SQL, FIXED, EXPR). The add-on is available as a licensed product for SAP Business One customers.

Stack: SAP Business One, SAP Business One Service Layer, coresuite Country Package, C#

• 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

• Development of software and system support for RPG development.
• Development, optimization and maintenance of programs in RPG (RPG II to ILE Free Format).
• Performing integration tasks, data management as well as applying and managing system updates.
• User support and training to ensure smooth system operation.

• Optimization of the transmission media of the data links in the Ruhr area
• Configuration/expansion of switches and routers for an autonomous system AS
• Improve the network protocols used Layer 2/3 (OSI model), RSTP, broadcast, etc.
• Laying cables, crimping, antennas, routers, switches
• Maintenance, expansion, selection and prioritization of troubleshooting measures for network monitoring (PRTG, The Dude)
• Organization and new connection of another BGP peering, data center, NGN fiber optic
• VPN IPSec site connections for 6 sites with PFSense, Fritzbox
• Configuration of WLAN devices (Mikrotik, UBNT

Label: TCP/IP, VLAN, BGP, OSPF, MPLS, VPLS, routing protocols, WLAN, EoIP, PPPoE, routing, SNMP, DHCP, DNS, NAT, native IP, VLAN, Ethernet, VoIP., switches and routers from: Nortel, Cisco, MikroTik, UBNT., network management systems: The Dude, PRTG, UISP

• Development of an automated job-scouting workflow for the aggregation and LLM-based evaluation of job postings from RSS feeds and APIs.
• Development of a Telegram bot for voice and text messages, featuring LLM-supported processing, summarization, and structured JSON output.
• Implementation of a personal task-planning assistant using n8n, OpenRouter, and Supabase for automated daily and knowledge organization.
• Development of LLM chatbots for structured knowledge retrieval utilizing prompt constraints, guardrails, and forced output formats.

• Controlling project work: implementation of customer quotation calculation and development of a trading and forward calculation for sales
• Development of a cost of goods manufactured calculator
• Theoretical design and practical implementation: temporary Excel-based solution introduced quickly; embedding into INFOR LN and creation of the requirements specification
• Majority of project management using modern tools (Loop, Jira, Excel, PowerPoint)
• Regular reporting on quotes submitted (contribution margin 1 and full cost coverage) and comparison with post-calculation

• Investigate the relationship between Eddy Current Testing (ECT) signals and microstructural properties
• Detect latent patterns in ECT data that reflect intrinsic material characteristics
• Develop and validate predictive models for microstructural classification and quantification, using hardness and case depth as benchmarks
• Apply Bayesian Structural Equation Modeling for advanced data analysis

• Technical lead of a team developing agentic chatbot solutions (React, TypeScript, Python, FastAPI)
• Architecture design for multi-LLM dialog systems - focus on maintainability, UX, and autonomous execution
• Stakeholder alignment, CI/CD processes, and AI integration at enterprise level

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

• Built a central monitoring solution to track the Business Central Job Queue in a multi-organization environment in Azure DevOps
• Integrated Azure Application Insights and Log Analytics for 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 status in real time
• Implemented an automated alerting workflow with Power Automate – when a job fails, a notification email is sent immediately
• Ensured transparency about the state of productive background processes during live operations

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

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

• Valuation support; analysis of assets, ownership/intercompany structures, reporting, and asset-transfer issues

• Further development and release support for a white-label ePA component for health insurance companies
• Technologies: Android (Kotlin), Kotlin Multiplatform, Coroutines, Jetpack Compose, MVVM, StateFlow, Gradle, ProGuard