AWS CDK Experts in Germany

I designed and developed an AI-powered multi-tenant platform on Azure that transforms SAP process recordings into technical documentation, presentations and automated tests, processing over 15,000 process recordings for enterprise customers like Würth. I owned the architecture, the production releases and the DevOps setup. I also designed a multi-tenant system with SSO and role-based access on Azure. Implemented an MCP Server with Dynamic OAuth Authentication.

Main Tasks:

• Sprint planning and feature preparation
• Design the multi-tenant platform architecture (FastAPI, SQLAlchemy, PostgreSQL row-level security for tenant isolation)
• Develop AI pipelines with Prefect for transcription (Azure Speech API), document generation and SAP screen-recording analysis (Claude, gpt-4-mini)
• Design and implement an MCP server to expose tenant knowledge to LLM clients (Claude), with async retrieval and reranking
• Implement LLM cost tracking, rate limiting and client pooling for Anthropic/OpenAI/Azure OpenAI endpoints
• Set up CI/CD: Docker images to Azure Container Registry, GitHub Actions, Azure Static Web Apps, Alembic migrations in containers
• Manage production releases and execute live data migrations for enterprise customers
• Define engineering standards and architecture patterns for the team

Environment: Azure / Azure Foundry / Python / FastAPI / Prefect / React / PostgreSQL

• 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).

• 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.

traigo is VTG's digital rail logistics and fleet management platform. It processes large volumes of telemetry, mileage, geofence, sensor and wagon-movement events in near real time and provides operational services for rail logistics customers across Europe.

As part of Team Customer Selfcare, I worked on the design, implementation, optimisation and operation of large-scale backend services and event-driven processing pipelines — covering both feature development and operational ownership of business-critical production systems. I also regularly acted as first responder for production incidents, data inconsistencies and performance investigations across multiple distributed services.

• Design and implementation of event-driven backend services.
• Migration and replacement of legacy processing pipelines.
• Development of replay / rebuild mechanisms for large event datasets.
• High-throughput asynchronous event processing on SNS / SQS.
• Database and query optimisation for PostgreSQL and DynamoDB.
• Design of scalable read / write models and aggregation pipelines.
• Production troubleshooting and operational support.
• Performance tuning and infrastructure scaling.
• Design and stabilisation of integration and system tests.
• Technical concepts, architecture documentation, and cross-team collaboration.
• Support the further development of existing GitLab CI/CD pipelines

Geofence & Wagon Stay Processing

• Algorithm to detect vehicles within geofences (entry, exit, dwell time).
• Event sourcing with guaranteed chronological order within the affected time window.
• Refactored geofence event and wagon-stay processing logic for performance.
• Resolved race conditions and event-ordering problems in distributed services; server-side filtering, aggregation and optimised query pipelines.
• Repair and replay tooling for corrupted or inconsistent movement data.

Fleet Metadata & Mileage

• Modernised the service; migrated storage from DynamoDB to PostgreSQL to improve traceability and accelerate new features.
• Scalable mileage aggregation and replay mechanisms.
• Read / write models and optimised queries for high-volume mileage calculations.

Sensor & Telematics Integration

• Integrated telemetry and sensor processing pipelines.
• Snapshot and state-calculation logic for sensor systems.
• APIs and persistence models for wagon sensor data; data-quality improvements.
• Further development of a service using gRPC for intra-service communication.

Movement Segment Processing & Routing

• Migrated services to new movement-segment event streams.
• Built replay and rebuild tooling for segment correction.
• Optimised throughput and reliability for high-volume event processing.

Condition Monitoring & Wagon Analytics

• APIs and backend services for wagon condition monitoring.
• Brake-wear prediction processing and wagon analytics functionality.
• PostgreSQL views and optimised query models for operational dashboards.

Operational Reliability - First Responder

• Investigated production incidents and distributed-system failures; DLQ analysis, replay and operational recovery.
• Tuned database performance and AWS infrastructure under production load.
• Improved observability, monitoring and operational tooling.
• Supported rollout strategies, monitoring and post-deployment stabilisation.

• Building new and improving existing features for a housing management platform that helps both landlords and tenants
• Technologies: Node.js, TypeScript, React.js, Next.js, PostgreSQL, Google Cloud, Docker, Kubernetes

• Building an independent enterprise IT setup — cloud strategy, network, AWS landing zone, security requirements, contract negotiations.
• Migration of all applications; avoiding high contractual penalties for the client.
• Onboarding and coordination o...

• Helped to design the AWS infrastructure, integrated services and backend architecture for use cases of an on-premise solution and partial migrations to AWS with fast response times
• Implemented automated AWS integration test suites
• Implemented mission-critical components and delivered them before the deadline in a production-ready state with operation and monitoring concepts
• This 2-month subproject was about building a data-intense pipeline (5 TB) to be enriched continuously with data
• Designed and implemented reusable AWS CDK constructs to be used across the company’s teams to enable faster onboarding with AWS
• Coached on AWS topics, distributed software patterns, security, domain-driven design, agile collaboration and documentation to improve performance and collaboration
• Technologies: AWS, GitHub Actions, ETL, monitoring, operations, TypeScript, Python, AWS CDK, CloudFormation, Java, Docker, AWS ECS, AWS Lambda, serverless, Jenkins, DevOps principles

• Bootstrapping a CloudOps team and building a multi-cloud provider backend for a low-code Internal Developer Platform (IDP) with env zero

• Introducing user story mapping, ADRs, milestones, and backlog management

• Designing and developing core APIs, setting up CI/CD pipelines, OpenTofu/Terraform scripts

• Representing and communicating the team with third-party stakeholders (e.g. env zero)

• (Cross-)team coaching on DevOps, software design, Terraform, Golang, and agile practices

• 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

• Build a scalable multi-region backend service in AWS to serve remote desktop virtual machines for scientific analysis
• Stack: AWS, GitHub, Terraform, Python, Rust
• Built and defined the core infrastructure of the backend system
• Defined and coded the virtual machines provisioning supporting Ubuntu and Rocky Linux desktop setups
• Programmed the API service running in ECS to manage virtual machines and build custom Docker images for users

• Built and operated an AWS Landing Zone with Terraform / OpenTofu (multi-account structure, IAM baselines, network and security standards)
• Designed and operated platform-oriented AWS architectures to standardize infrastructure and operations processes
• Built and operated Kubernetes-based platforms (EKS) as a shared runtime environment for application teams
• Established GitOps-based deployments with Argo CD and FluxCD
• Developed and operated central CI/CD platforms (GitLab CI, GitHub Actions, Jenkins)
• Enabled developer and project teams with reusable platform components
• Introduced and implemented FinOps structures (AWS Cost Explorer, CUR + Athena, Infracost, Grafana dashboards)
• Built and operated central observability platforms (Prometheus, Grafana, Loki, Alertmanager, CloudWatch)

• Development of a microservice application for calculating postings on accounts

• Development of backend components with Java or JEE, Quarkus, Kafka, Rest API

• Development of the test component with mocking frameworks (Mockito)

• Development of integration test components

• Creation of OpenAPI specification format

• Configuration of CI/CD pipeline

• Development of frontend components with React

• Design, further development and optimization of system structures

• Code review

• Java Corretto 21, JEE, Quarkus, Golang

• Reactive programming with Spring WebFlux, Project Reactor

• Git, Azure DevOps for CI/CD pipeline, YAML, Json Swagger UI, Postman, Rest OpenAPI, Kafka Streams, Kafka Connect, Log4J

• Microservices, Jira, Confluence, MySQL, Docker, Kubernetes (deployment via Helm charts)

• Spring Security, OAuth 2, JWT, Sonarqube

• IntelliJ, Maven, logging framework Splunk, Agile method Scrum, Nexus, Vaadin V24, build tool Vite

• React, React.JS, Node.js, Jess/JavaScript, Tailwind CSS, Agile method Scrum, accessibility BITV, Playwright

• Built an API to ingest, clean, translate, and index EU tenders documents in Neo4j, enabling hybrid search with RAG and Cypher queries via a Streamlit dashboard
• Deployed the API on AWS Lightsail container services with CI/CD automation via GitHub Actions, ensuring stability through pytest unit and integration tests
• Designed and developed a comprehensive online course on data analysis using ChatGPT for professionals and learners, creating instructional videos and interactive Jupyter notebooks
• Utilized OBS and professional audio equipment to ensure high-quality video and audio content
• Led a CRM data normalization and cleaning project visualized via a Sankey diagram to aid customer understanding and pipeline development
• Implemented and validated a genAI-driven web crawling strategy on AWS, ensuring data quality, scalability, and CRM data augmentation