RabbitMQ Experts in Munich

Unified ITSM framework

• Definition of a company-wide ITSM target picture
• Introduction of a uniform service structure across all business units

SLA and OLA management

• Building a standardized SLA framework
• Definition of service classes (Business Critical, Standard, Low Priority)
• Introduction of OLAs between internal teams
• Building meaningful SLA reporting
• Definition of KPI and service dashboards for business units

Service portfolio management

• Definition of service descriptions
• If needed, preparing possible cost and service billing

Ticketing & processes

• Incident management
• Uniform ticket categories
• Standardized prioritization
• Escalation matrix
• Automations
• Self-service optimization

Request fulfillment

• Service catalog across all business units
• Approval workflows

Problem management

• Introduction of root cause analysis
• Known error database
• Problem review process

Complete asset management concept

• Hardware lifecycle management
• Software lifecycle management
• Leasing lifecycle
• Mobile device lifecycle
• Monitor lifecycle
• Phone lifecycle

Processes

• Procurement
• Goods receipt
• Inventory
• Assignment
• Return
• Disposal
• Leasing return Goal: single source of truth for all assets

CMDB design

• Definition of all configuration items:
• Workplace
• Notebooks
• Monitors
• Mobile phones
• Printers

Infrastructure

• Servers
• Firewalls
• Switches
• WLAN
• Storage
• Backup systems

Cloud

• Azure resources
• Microsoft 365
• SaaS services

Relationships

• User ↔ Asset
• Asset ↔ Service
• Service ↔ Infrastructure
• Location ↔ Asset
• Goal: make all service dependencies visible

Software asset & license management

• License management concept
• License balancing
• Compliance reporting
• Microsoft license management
• Adobe license management
• SaaS management
• Contract management
• Renewal management

Interfaces & automation Existing systems

• Workday
• Joiner
• Mover
• Leaver

TESMA

• Leasing data
• Contract data

Matrix42

• Asset synchronization
• User synchronization

Active Directory / Entra ID

• User management

Microsoft 365

• License assignment
• Group management

Dormakaba

• Access processes

• Lifecycle services

Monitoring platforms

• PRTG
• Palo Alto
• Cisco

Reporting & KPI framework

• Definition of a management dashboard
• KPIs
• Ticket volume
• SLA fulfillment
• MTTR
• First resolution rate
• Asset accuracy
• License compliance
• Change success rate
• Service availability
• Degree of automation

Network redesign support

• Governance
• Support of the network redesign from an ITSM point of view
• Definition of affected services
• Change management structure
• Communication concept

CMDB integration

• Recording of all network components
• Service mapping
• Dependency analysis

Validation of documentation and knowledge base articles

• Network documentation
• Operations documentation
• Standard changes

Monitoring & event management

• Target picture
• Central monitoring concept
• Event management process
• Alerting strategy
• Escalation model

Systems

• Cisco

• Palo Alto

• Fortinet

• Rubrik

• Veeam

• Matrix42

• Azure

• Microsoft 365 Automation

• Ticket creation from monitoring

• Escalations

• Standard actions

Audit, compliance & information security

• ISO 27001 consulting
• TISAX consulting
• NIS2 preparation - consulting
• Audit-ready processes
• Documentation structure
• Evidence tracking in Matrix42

Roadmap

• 12-month roadmap
• Prioritization of all measures
• Quick wins
• Medium-term projects
• Long-term target picture
• Documentation

• Build and operate an end-to-end MLOps platform on Azure ML and Kubernetes (Kubeflow) for the automated deployment, monitoring, and scaling of forecasting models (including Temporal Fusion Transformer, Informer, Autoformer).
• Implement CI/CD pipelines in Azure DevOps for the full ML lifecycle – from resource provisioning (Terraform), data transformation (Hugging Face Datasets, Pandas, PyTorch, CUDA cluster) through training and evaluation to model registry and endpoint deployment.
• Integrate MLflow for experiment tracking, model versioning, performance monitoring, and automated registration in the Azure Model Registry.
• Develop and containerize PyTorch training jobs (Azure Notebook, Jupyter Notebooks) for price and time series forecasting (PFC models) with automatic rollout via Azure ML Endpoints and REST/gRPC interfaces, Docker containerization, secured with OAuth 2.0.
• Set up monitoring and alerting mechanisms (Prometheus, MLflow Metrics), log centralization, and cost monitoring.
• Automate infrastructure provisioning and model deployment using Terraform, Helm, and Azure CLI; connect to existing market data systems and event pipelines.
• Migrate existing workloads and databases (IONOS → Azure, MongoDB) with integration into central MLOps workflows and internal networks.
• Extend the platform with LLM-based tools (LangChain, LangServe) to integrate GPT-based analysis modules into existing Spring Boot services for market anomaly detection and automated reports.
• Analyze and architect a software solution to process large volumes of data efficiently (>3000 messages/sec.) (market data store).
• Spring Boot / Java 21 container development with RabbitMQ for distributing stock market data via MongoDB (Kubernetes) with fast storage of data in Redis RMaps, deduplication, forwarding messages to Read Model queues, and building Read Models for UI display in MongoDB.
• Integration of RESTHeart to create a REST API for MongoDB.
• Build an Angular frontend to simplify data queries and master data maintenance.
• Agentic coding with remote and local LLMs (Claude Sonnet, Ollama Qwen) and MCP servers.
• Develop Python scripts for transforming and cleaning incoming stock market data (Pandas, scikit-learn).

• Managed end-to-end product development, overseeing the successful delivery of technical solutions.
• Led and mentored a team of highly specialised technical professionals, fostering a culture of collaboration and innovation.
• Oversaw the hiring process to build a talented and dedicated team.
• Built a scalable and robust backend microservices system from scratch, designing and extending it to meet evolving business needs.
• Ensured the system's high availability with a 99.99% up time, implementing resilient architecture and monitoring mechanisms.
• Developed and implemented technical strategies, aligning them with business goals and objectives.

• Redesign of a legacy Windows app for vehicle diagnostics as an AWS cloud application
• Creating build pipelines and conducting code reviews using Kotlin, Spring Boot, Micronaut, Jenkins, and GitHub

Task

• Further development of a new ERP system with Blazor
• Creation of e-invoices in the XRechnung and ZUGFeRD formats
• UI/UX design Client-server system
• Windows 11 Technology
• Microsoft .NET 9, Git, Azure DevOps, ASP.NET, MSSQL, Blazor Programming languages
• C#, MVVM Development tools
• Microsoft Visual Studio .NET 2022 Database
• MSSQL Industry
• Other

• Implementation of a GitLab CI/CD pipeline based on SAP Commerce Cloud 2211
• Development of a new B2C shop with Vue, Node and TypeScript to ensure scalability, extensibility and performance optimization
• Integration of Microsoft Azure Event Grid with SAP Hybris
• Implementation of BDD with Cucumber using Gherkin syntax to promote collaboration between development and business teams
• Support and customization of a Spartacus shop
• Technologies/Languages: Java, REST, OData, Behavior Driven Development (BDD), Cucumber, Node, Vue, TypeScript, Spartacus, GitLab CI/CD, Gradle, Maven, Ant, SonarQube

• Development of Blazor-Server applications for data management
• Focus on unifying design and user experience
• Mentoring junior developers
• Establishing and consulting for coding guidelines and best practices
• Technologies: .NET 9, Blazor Server, MudBlazor, HTML, CSS, MS SQL Server (T-SQL, Views, Trigger, Stored Procedures, Table-Valued Functions), Entity Framework, Visual Studio, Scrum, agile, git

• Participated in the Riyadh Public Transport (KAPT) project introducing public transportation in Riyadh, supporting multimodal journeys combining bus, metro, car on demand.
• Worked via Thiqah and partnered with Andersen to refactor the mobile MaaS application DARB into a new microservices architecture under SCRUM framework, successfully completing Phase 1.
• Facilitated a team of 40 developers across Android, iOS, Web, Java, DevOps, architecture, analysis, and QA.
• Moderated Scrum events and communicated with the client, proactively reporting on project deadlines, scope, and challenges.
• Coordinated cross-team efforts between Thiqah specialists and GPS development teams, managing Jira and Azure DevOps trackers.
• Supported and improved Scrum processes throughout the project.
• Due to Thiqah’s takeover by Elm, subsequent phases were handled entirely in Saudi Arabia with GPS providing IT consulting.
• Tools: Azure DevOps, Jira, Confluence, Draw.IO, ChatGPT

• Migrated the Electric Vehicle Charging SaaS App of the EV Division from on-premises and Azure to AWS Cloud, resulting in a hybrid multi-cloud multi-tenant solution
• Developed a streaming data pipeline using AWS MSK for Apache Kafka and implemented an event-driven architecture to ingest and process near real-time data from OCPI-protocol IoT devices
• Implemented multi-tenant strategies including database schema isolation, bridge model for resource sharing, and tenant-based RBAC controls
• Provisioned Kubernetes clusters on AWS EKS with namespaces and RBAC for tenant isolation
• Led migration from on-premises and Azure to AWS using AWS DataSync, Snowball, and Database Migration Service
• Orchestrated collaboration across 5+ systems, vendors, service providers, and on-site teams
• Supported development and maintenance of IT strategy aligned with business requirements
• Managed €40 million infrastructure budget with AWS & Azure cost optimization, achieving 15% savings
• Led 50+ developers to implement advanced database procedures, increasing productivity by 20%
• Spearheaded multi-cloud, multi-tenant infrastructure migration for 30% faster processing times
• Negotiated vendor pricing to reduce payroll/benefits administration costs by 20%
• Developed a two-year infrastructure technology roadmap yielding 25% cost savings
• Tech stack: Kubernetes on AWS EKS, Docker, Kafka/AWS MSK, Terraform, AWS CDK, TypeScript, React, NextJS, Node.js, NestJS, Python, Aurora Serverless, RDS (MySQL, SQL Server), GitHub Actions, Azure DevOps, ArgoCD, AWS Lambda, API Gateway, AWS Security Hub, AWS Database Migration Service, AWS DataSync, AWS Organizations, AWS Control Tower, Odoo, Microsoft Navision, MS Dynamics

• Role: Senior FullStack Developer
• Technologies: Google Cloud, Azure, Microsoft Entra, Keycloak, Spring Boot, Java
• Active in production

• Building the company with a focus on connectivity for IIoT and Industry 4.0, iRPA/process automation, advanced robotics and smart systems, sensors and services
• Project management and software architecture for IoT gateway development (since 2020) with protocol translation, IT/OT convergence and GRC
• Developing RPA bots for automating and monitoring industrial processes with an agent-based AI approach (since 2020)
• Implementing unsupervised clustering and anomaly detection for time series data in big data streaming pipelines (since 2021)
• Introducing a Docker-based release train for OTA updates with DevSecOps and CI/CD (since 2018)

• Developed dynamic web components for displaying KPIs, intelligent map applications, and operational process analysis tools
• Angular 18+
• Leaflet, MapLibre
• NestJS, JavaScript, HTML, CSS
• PostgreSQL, GraphQL, RabbitMQ
• Gitea, Jenkins, Docker

• Requirements analysis and planning of the software architecture
• Analysis and design of REST APIs
• Backend development with Java 17, Spring Boot, Spring MVC, and Spring Data
• Frontend development with Angular and TypeScript
• Setting up CI/CD pipelines
• Code review, QA, and testing
• Using MySQL, Docker, the ELK stack, and RabbitMQ

• The software supports the complete planning and approval of antennas for mobile telephony.

• The system was implemented using an event-driven microservice architecture for cloud-native deployment with Quarkus on the backend, Kafka for communication, and Angular for the frontend. Services run on Kubernetes in Google Cloud. A special challenge was synchronizing with the legacy system still used by some users.