Amazon Web Services Experts in Dortmund

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.

• 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

• Delivered MVP-focused full-stack architecture for a health-sector client: Vite/React frontend, backend services on Google Cloud Run, and Supabase for database plus IAM/authentication.
• Supported product requirements engineering and prioritized cost-aware workload placement, implementing browser-side/edge computation where feasible before moving logic to backend services.

• 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.

• 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.

• 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

• Setting up and consulting a startup for organizational development (AI-supported)
• Vision and mission workshops
• Marketing roadmap and process design
• Development of sales processes (sales funnel, lead generation)
• Brand building and social media omnichannel strategies
• Content generation for audio, video, and posts (AI-supported)
• Agile consulting and SAFe implementation
• Atlassian consulting and setup of Confluence and Jira (workflows, dashboards, reporting)
• Setup of a podcast and video studio including live streaming setup
• Selection and implementation of hardware and software
• Website creation and brand building
• CMS implementation based on Typo3
• Consulting in sales, marketing, and social media
• Supporting companies in the efficient use of AI technologies
• Strategy development and consulting on implementing AI strategies
• Change management and supporting teams in AI adoption, including training
• Technical expertise in machine learning, data processing, and large language models (LLMs) like Llama, Claude, and OpenAI GPT
• Selection, fine-tuning, and integration of LLMs into business processes
• Consulting on ethics and governance for responsible AI use
• Conducting tailored training for executives and teams
• AI analysis of various large language models (Llama, OpenAI, Mistral)
• Creation and training of custom GPTs focused on organizational development (product vision, OKR, agile maturity)
• Development of a chatbot on agile maturity
• Development of an AI-generated survey to assess organizational agility
• Setup of local offline GPTs with a focus on data protection and GDPR compliance
• Conducting AI workshops for SMEs and mid-sized companies

• Designed and implemented the system architecture for scalable document workflows, improving stability and performance.
• Optimized the login flow with TOTP-based MFA, reducing complexity and login time by 35%.
• Built secure backend APIs for document upload, signing, and management.
• Developed encrypted file handling with AWS S3 pre-signed URLs, ensuring GDPR-compliant data protection.
• Implemented JWT-based role authentication, strengthening access control and overall platform security and reducing code and database complexity.
• Optimized database queries and caching using ORM, reducing API response time by 40%.
• Integrated real-time webhook updates, enhancing workflow transparency and user experience.

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

• Teaching JavaScript, HTML, CSS, React, NodeJS, MongoDB, MariaDB, and Express
• Designing and preparing lessons
• Classroom teaching and one-on-one coaching

• Time off
• Process building and automation