GitHub Actions Experts in Munich

Temporary employment

• System black-box integration tests (BBIT, IVVQ): Execution of regression, release, acceptance, and compliance tests for safety-critical brake control units in the rail industry
• Software test application & integration: Runtime configuration of software components and libraries, validation of interfaces, configuration dependencies, and component interactions
• Test automation (FEAT framework): Co-development and further development of an automated test framework for test execution, reporting, and result analysis
• Functional safety (SiL4, FuSi): Ensuring compliance with safety requirements, traceability and coverage, as well as standards compliance according to EN50126/28/29
• Test automation for communication components: Configuration and validation of fieldbus (CAN) and Ethernet-based TCMS data communication interfaces (TRDP and CIP)
• Requirements analysis & shift-left (PTC Windchill ALM): Analysis of software and system artifacts to identify gaps, ambiguities, and redundancies early in the SDLC
• Test design & test case development: Derivation of test conditions, coverage strategies, and implementation of data-driven test cases (DDT), including reusable test data fixtures
• CI/CD & automation (Python, PowerShell, Jenkins, SVN): Automation of build, test, and HIL deployment processes as well as integration into CI/CD pipelines
• Test data & configuration management (XML): Maintenance and adaptation of XML test vectors and system configurations with automated integration into test environments
• Non-functional testing: Execution of performance and load tests to assess stability and system behavior
• Agile development & defect management (JIRA, Confluence): Participation in Scrum teams, test coordination, review of test artifacts, as well as defect tracking and root-cause analysis
• Error analysis & debugging (CANoe, CANalyzer): Analysis of errors and message flows across multiple system layers (application to bus)
• Model-based analysis (UML, Enterprise Architect): Specification of SUT/SOW and support for systematic test control
• Process & test documentation: Creation of integration and test documentation according to internal quality and certification requirements

• AI Integration (LLM & RAG): Design and build of an internal intelligent RAG system (Retrieval-Augmented Generation) based on LLMs, n8n, and vector data for the automated analysis of technical documents and error logs.
• Design & Implementation: Design of a robust RS-232/UART communication interface for an SBC-based embedded device to control medical shaker systems.
• Architecture & Protocol Design: Implementation of a highly maintainable software structure (OOP, SOLID) and definition of hardware-close, resilient communication protocols including multithreading and advanced error handling.
• Quality Assurance & DevOps: Test automation using xUnit, integration tests directly on the hardware target, and maintenance of technical documentation according to strict medical technology standards via Azure DevOps.

Label: C#, .NET, LLMs, RAG, n8n, RS-232, UART, Multithreading, async/await, xUnit, gRPC/protobuf, Blazor, MudBlazor, EF Core, Visual Studio 2026, Azure DevOps

• Created a multi-agentic system supported by a knowledge graph to automate drafting of research papers
• Used multiple experts (OpenAI models) collaborating during document drafting
• Extracted useful information from the knowledge graph
• Technologies: LangChain, LangGraph, Smolagents, LlamaIndex, dspy
• Infrastructure: Terraform and GitHub Actions (CI/CD) on AWS
• Deployed initial application as a Streamlit app

• 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).

Project: Tax Exemption Order Application

The client has an existing application for creating and maintaining tax exemption orders for end customers; design and implementation of a comparable application for internal employees.

• Design and implementation of microservices and the UI for the business area "tax exemption orders" using Domain Driven Design as well as Spring Boot and Angular.
• Implementation of reactive, non-reactive, and asynchronous APIs (Spring REST, WebFlux, GraphQL).
• Development of the Angular application, including state management using Signals, RxJS Observables, and subscriptions.
• Securing the API and the application using OAuth2, JWT, and OpenID Connect.
• Configuration and setup of CI/CD pipelines with Jenkins.
• Collaboration with cross-functional teams and conducting code reviews.

Environment: Java, Spring Boot, Angular 18 & 19 (standalone, signals), RxJs, Bootstrap CSS, Vitesting, OpenShift, Istio, microservices, Kafka, Dynatrace, Jenkins, GitLab, Graylog, Sonar, Oauth2, OracleDB

• Built a Python pipeline that converts lectionary web content into structured daily JSON, applying liturgical-calendar rules for accurate weekday and Sunday coverage.
• Shipped a read-only FastAPI REST API with shared Pydantic models and delivered a Kirundi-first web client for browser and mobile use.
• Owned the data layer and backend architecture, collaborating closely on system architecture and interfaces while automating refreshes with GitHub Actions and validating the ETL with pytest.
• Impact: Created a reliable, API-driven source of truth for daily Bible readings in Kirundi, enabling consistent access to previously unstructured content.

• It is like a startup within REWE, where we have to build a new forecasting system on Google Cloud Platform from the scratch. Although, officially my role is called MLOps, my actual tasks also include development of data processing pipelines (data engineering) and data scientists tasks such as feature engineering and model trainings.
• GCP: Terraform (tofu), Vertex AI (Kubeflow), Cloud Run, IAM, Google Cloud Storage, BigQuery, Artifact Registry
• Data engineering: Snowflake as the main data warehouse, Terraform, DBT for data model implementations
• CI/CD: GitLab. We have built a CI/CD pipeline that automates deployments of new releases up to production environment

• Feasibility study for quick implementation of requirements with AI-based development (vibe coding)
• Development of a single-page web app in Angular 20
• Development of a backend server application in Kotlin
• Integration with Google Cloud Platform (Firebase): authentication, Firestore NoSQL database, storage, Cloud Functions, hosting and Cloud Run
• Integration with a NEON PostgreSQL database
• Automated AI-based analysis of Donald Trump's posts on Truth Social and analysis of relevance for stock markets and geopolitical topics
• CI/CD via GitHub Actions using Docker and Google Cloud Run
• Technical environment: Angular 20 (Angular Material, RxJS), Kotlin 2.2.20, TypeScript 5.9.3, Spring Boot 3.5.6, Google Cloud Platform (Firebase, Cloud Run, Gemini, Vertex AI), ChatGPT Codex, Git, GitHub, SourceTree, IntelliJ WebStorm, IntelliJ IDEA

• Responsible for Openshift/Kubernetes on-prem administration and developer support.
• Developed URP infrastructure automation with Python, Ansible, Kustomize and ArgoCD, Argo Workflow/Events stack.
• Wrote smoke and load tests for URP infrastructure utilizing Python, Kustomize and ApplicationSets.
• Helped to set up and deploy URP infrastructure in Google Cloud, GKE.
• Set up monitoring for URP and ArgoCD stack with Splunk Cloud.
• Performed system administration tasks across RedHat Linux, Kubernetes/Openshift, ArgoCD, GitLab, Bitbucket Enterprise, Kafka and MongoDB.

An enterprise process mining and execution management platform that helps organizations analyze, visualize, and optimize their business processes using event data.

• Aligned 40+ engineering squads across 5 countries to deliver platform capabilities at scale, driving release infrastructure, change management, and rollout governance with zero post-launch defects for 1000+ enterprise customers
• Owned celonis studio platform roadmap prioritization, balancing technical investments, commercial priorities, and operational needs, driving 50%+ market share growth against legacy products
• Translated requirements into specifications, architectural decision records, and delivery plans in collaboration with engineering and UI/UX, resulting in a 20–30% increase in user engagement
• Drove the GA launch strategy, running pilot programs and customer discovery sessions to validate operational readiness and enterprise adoption

• Development of an accessible, responsive website based on Figma designs for a major sports event in compliance with WCAG, BITV, and EN 301 549
• Technical planning and setup of the tool stack with Vite, Playwright, Docker, and GitLab CI
• Worked on two existing projects (Vue.js & Nuxt), with a focus on bug fixing, accessibility audits, and new feature development
• Collaboration with design and QA to consistently implement accessible UI patterns
• Consulting on improving existing frontend architectures with regard to accessibility, performance, and maintainability

• Concept creation and implementing AI Agents in AWS Cloud
• Continuously alignment with stakeholders
• Collaborate with DevOps
• Technologies: Git, CI/CD (GitHub Actions), Python/ML, Streamlit, Deno/typescript, AWS SAM, AWS Bedrock, AWS Lambda, AWS Dynamo DB, AWS S3, AWS Event Bridge etc.

• Development of a test system
• Establishment of the architecture using Scala/Java for the backend and Swing for the frontend
• Setup of CI/CD pipelines with Jenkins for continuous integration, including automated builds
• Participation in the Scrum team, including daily stand-ups, sprint planning, and retrospectives
• Technology environment: Scala, Swing, EJB, JPA, Scrum, PostgreSQL, Mercurial

• Designed and developed the "Credit Risk Engine" using Camunda, Node.js and Typescript
• Greenfield project for credit card credit assessment for existing and new customers, including EBA KPIs, SCHUFA and CRIF scorings
• Built and modeled workflows (BPMN) and decision logic (DMN) with Camunda Modeler in close collaboration with stakeholders
• Implemented service tasks, user tasks and jobs with Nest.js, Node.js and Typescript, including exception handling
• Backend-for-Frontend (BFF), frontend with React, Tailwind and Ant Design UI library
• CI/CD with GitLab, Kubernetes/Rancher