Data Visualization Experts in Dortmund

• 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

• Architected and deployed end-to-end machine learning pipelines across classification and prediction datasets, ensuring robustness and reproducibility through MLOps best practices.
• Contributed directly to LLM model output accuracy improvement by designing and engineering specialised prompts grounded in end-to-end ML and SciML pipeline logic.
• Developed training data for large language models by formulating coding problems that models could not resolve and subsequently documenting the correct solutions.

• 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

• ERP selection project at an automotive supplier in Regensburg to choose a successor system for a custom-built legacy solution
• Conducting interviews with specialist departments
• Creating a criteria catalog
• Organizing and running workshops

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

• Developed a reporting API with RESTful services and Spring Boot
• LDAP integration
• Migrated services to AWS
• Bootstrap
• Frontend development with Angular 14
• JBoss
• DynamoDB
• S3
• Athena