Amazon Cognito Experts in Germany

• Development and introduction of operational processes
• Preparation of complete documentation packages (including emergency management and operations) to meet compliance requirements
• Introduction of a workshop on IaC (Infrastructure as Code)
• Professional consulting for the project's security concept (ISMS)
• Installation and operation of Kubernetes clusters on AWS, on-prem, and Azure
• Design of hybrid cloud architecture (on-prem, Hetzner, AWS)
• Analysis and resolution of incidents and system outages
• Network changes to firewall rules, gateways, OpenVPN settings, and IPsec tunnel (pfSense)
• Professional consulting on BitBucket, Jenkins, and GitLab CI/CD pipelines
• Consulting on Ansible deployments and infrastructure automation
• Consulting on building a scalable system in the cloud (AWS / Azure)
• Technologies / Tools: Ansible, Terraform, AWS, Azure, VPN, pfSense, Jenkins, Bitbucket, Kubernetes, GitLab Runner, ISMS, Golang, Prometheus, Grafana, S3, Lambda, RDS, ECS, Cognito, OIDC, Harbor, MinIO, Postgres, Redis, Keycloak, Ceph, Proxmox, CloudFormation, PostgreSQL, Flux CD, Hetzner, IONOS, Sonatype Nexus Repository, Entra ID, Dex IdP, Pulumi

Conversational AI assistant for cloud infrastructure and security queries

• Designed FastAPI backend with multi-turn conversation handler, token budgeting, and context window management.
• Integrated Amazon Bedrock (Claude 3 Sonnet/Haiku); built RAG pipeline with MongoDB chat history and semantic search.
• Implemented Factory Pattern for modular LLM provider switching; reduced model onboarding effort by 60%.
• Reduced LLM inference cost by 35% through model tiering (Haiku vs Sonnet) and prompt/entity consolidation.

Tech: Python, FastAPI, Amazon Bedrock, MongoDB, Streamlit, Pydantic.

• 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).

Design and implementation of a compact ERP, CRM, accounting, and production-planning platform for a German food manufacturer. The system replaces Rechnung11, self-built Excel sheets, and manual processes for fewer than 10 internal users.

• Designed and implemented the full platform architecture as sole architect and developer.
• Built modules for customer management, B2B order handling, invoicing, production planning, and accounting support.
• Implemented DATEV export, ZUGFeRD/XRechnung e-invoicing, FinTS bank statement synchronization, and GoBD audit trail concepts.
• Used AI-supported workflows for prototyping, test support, and implementation acceleration while retaining full architecture, review, testing strategy, and technical ownership.

Technology: Java 23, Spring Boot 3, Spring Data JPA, Spring Security, Vue 3, TypeScript, PostgreSQL, Flyway, REST, OpenAPI, JWT, TOTP, RBAC, DATEV, FinTS, ZUGFeRD, XRechnung, GoBD, JUnit, Mockito, Testcontainers, Playwright, Docker, GitLab CI/CD

• Independently designed, developed, published, and maintained mobile games for iOS and Android.
• Implemented application architecture, gameplay systems, UI, monetization, analytics, and platform integrations.
• Managed the complete release lifecycle, including testing, store publication, production monitoring, and iterative improvements based on analytics.

• Developing Kotlin/Spring Boot microservices deployed on Kubernetes and AWS Lambda
• Implementing monitoring solutions
• Backend development for Traigo platform (booking/analysis for freight wagons)
• Determining wagon stays in geofences
• Routing of position data
• Forwarding position data and geofence events to clients using ITSS
• Developing and deploying services in both Kubernetes and AWS Lambda environments
• Tech stack: Kotlin, Spring Boot, Kubernetes, AWS Lambda, PostgreSQL, DynamoDB, Protocol Buffers

• Extension and maintenance of the Doktree tax advisory document management tool
• Alfresco and Angular development
• Linux system maintenance

• Architected ETL services for event-driven data exchange between enterprise systems on a Kafka streaming backbone.
• Optimized CI/CD pipelines for Azure AKS deployments and improved OpenSearch monitoring and alerting for proactive incident detection.

Tech: Java / Kotlin, Quarkus, Kafka / Avro, Azure / AKS, Azure Storage Container, ArgoCD, GitLab CI, OpenSearch, Terraform

• Optimized the data architecture (medallion) to better decouple processing stages and improve transparency and reproducibility
• Ensured technical quality of data processing in Databricks by introducing schema enforcement, data quality checks and a structured data architecture
• Orchestrated pipelines with Azure Data Factory
• Professionalized and automated the development and deployment process by integrating Git and GitHub Actions
• Led the Data Engineering team (3 members) in a functional role
• Conducted workshops to optimize and stabilize the data platform and the development process
• Collected and prioritized new requests, maintained the product backlog
• Technologies: Microsoft Azure (Data Lake, Data Factory), Databricks, Apache Spark (PySpark), Python, SQL, Git, Confluence, Power BI, Power Apps, Dataverse, MS SharePoint, Mural

• Implemented asynchronous communication with Kafka Streams to update user and group data in microservices
• Conducted load and performance tests with JMeter to ensure system stability
• Migrated microservices to current versions of Spring Boot libraries for better maintainability and security
• Optimized Hibernate queries to improve database efficiency
• Deployed new microservices on AWS infrastructure using Kubernetes for scalability and fault tolerance
• Created OpenSearch dashboards to monitor and analyze system metrics
• Integrated Azure Entra user accounts via SCIM by implementing all necessary REST endpoints
• Improved response times through asynchronous processing in separate threads for better performance
• Enhanced Android device logging capabilities for easier troubleshooting
• Refactored database schema with Flyway for consistent and traceable migrations
• Updated AWS Cognito user settings to optimize user management
• Offloaded resource-intensive tasks from the main microservice into separate Docker containers to boost performance
• Used technologies: Java, JavaScript, Kotlin, Angular, Maven, Spring Boot, Spring Security, JUnit, Tomcat, Spring Data, Spring Cloud, Hibernate, Jackson, microservices, Docker, Kubernetes, OpenSearch, Grafana, OpenAPI, SonarQube, PostgreSQL, MySQL, JMeter, Kafka, Flyway, AWS, Azure, Jira, Confluence, Agile, Scrum, SAFe

• Responsible for the end-to-end application and integration architecture of the Hamburg S-Bahn maintenance digitization program. A highlight is the introduction of robots and fixed camera towers to automate vehicle inspections, allowing AI image algorithms to assess train conditions during operation.
• Deutsche Bahn has one of the largest SAP PM implementations worldwide, which is a key component of this digitization.

• Further development of an existing microservice architecture (REST) for asset model management and runtime environment in the cloud.

• Java development (BE) in the SCRUM team.

• Java (Spring Boot/Security)

• PostgreSQL

• Docker

• AWS Services (Lambda, Cognito, ECS, SQS)

• IntelliJ

• Role: Developer, Architect
• Technologies: java, typescript, golang, spring (boot, web, security, data), Angular, AWS (OpenSearch, Aurora, SNS/SQS, CloudWatch, EC2, IAM), Kubernetes, Terraform, Helm, OAuth, Keycloak, CI/CD, gradlew, Liquibase, Test Driven Development, shell scripting

• Delivered AWS-authorized content through AAI program
• Facilitated group and 1-on-1 mentorship sessions
• Coached non-technical learners into cloud career paths
• Created learning materials aligned with AWS Skill Builder and AWS Academy labs