Amazon DynamoDB Experts in Munich

• 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

• 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

• Create an AI legal assistant with modern ML capabilities.
• Implement RAG architecture, with data pipelines for legal data search.
• Use AWS Bedrock for LLM and embedding models and LangChain/LangGraph
• Python with FastApi for backend and React for frontend

• 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

• 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.

• Test concept creation
• Creation of test processes
• Coordination of TC development: stress tests, functional tests, performance tests, high data rate tests, integration tests, etc.
• HW testing: FPGA, RF
• Test automation and regression tests
• Ensuring 24/7 operation of the test system
• Analysis & reporting
• Regular coordination of the test team, meetings with other stakeholders
• Communication and coordination with stakeholders and the project manager

• Designed and developed a high-performance internal resource management platform using React and TypeScript, optimizing dynamic data rendering and state management.
• Built a React Native application to support mobile access to internal tools, enabling on-the-go project tracking for field teams.
• Developed custom 2D canvas-based visualizations using Pixi.js to simulate material flows and refractory layer behaviors.
• Integrated Pixi.js with React components for interactive diagrams and real-time UI updates.
• Developed interactive 3D visualizations using React.js for displaying refractory product layouts and simulations, supporting engineering and sales teams with dynamic product previews.
• Integrated Three.js within the React ecosystem to allow manipulation of 3D models in real-time via browser, enhancing user engagement and field configurability.
• Integrated a headless CMS to enable dynamic content updates by non-technical users, reducing content deployment time by 40%.
• Led AWS CloudFront optimization initiatives, improving portal load speeds by 30% globally.
• Actively collaborated with cross-functional Agile teams and product owners to deliver prioritized features with a fast feedback loop.
• Key Technologies: React.js, React Native, Three.js, TypeScript, Contentful CMS, AWS S3/Lambda/CloudFront, Cypress, Agile Scrum

I have worked across multiple international client projects, holding senior roles including Software Architect, Senior Software Developer, Technical Lead, and Lead Backend & DevOps Engineer. My experience spans complex enterprise environments in banking, financial services, telecommunications, engineering, and automotive domains, supporting organisations such as UniCredit Bank, Telefónica O2, and BMW.

At UniCredit Bank, within the Securities Domain Transformation program, I led the modernisation of legacy monolithic systems into cloud-native Spring Boot microservices and an Angular frontend deployed on Google Cloud Platform. Beyond implementation, I was responsible for defining the target architecture, producing system architecture diagrams and sequence diagrams, and preparing API contract documentation for clients. I designed RESTful APIs and integrated Apigee for secure and reusable cross-project service consumption of APIs. I architected Kubernetes-based deployments using Helm. CI/CD pipelines were built with Jenkins, automating code analysis using Sonar, as well as testing and deployment stages. Defining clean coding principles for the project, conducting regular code reviews, and mentoring junior developers were also among my tasks at UniCredit.

At Telefónica O2, I led the transformation of a legacy call centre desktop application into a cloud-native microservices and micro-frontend solution. I actively contributed to the platform architecture, creating system architecture diagrams, component diagrams, architecture documentation, and ADRs for future references. I improved the performance and scalability of the services. I optimised AWS infrastructure costs, particularly by minimising the use of DynamoDB and reusing test environments effectively. Observability was implemented using Prometheus, Grafana, CloudWatch, and Splunk dashboards. CI/CD pipelines were delivered using GitLab, Docker, Kubernetes, and AWS. Conducted techinical sessions for teams.

• Development of the Ibalis portal and payment procedures for applying for and paying out EU funding.
• Publicly accessible portal, e.g. for farmers.
• Projects over the last ten years were carried out according to Scrum.
• Technologies: Spring, Wicket, Hibernate, Postgres, JSON, XML, REST, Bitbucket, Podam, Buckets, JUnit, Gradle, Intellij, Bamboo, Swagger.

• 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)

• Architected and implemented an enterprise-grade AI Agent Platform leveraging Retrieval Augmented Generation (RAG) architecture to enhance clinical data insights.
• Established robust CI/CD pipelines for LLM applications using CDK and Jenkins, significantly reducing deployment times.
• Implemented comprehensive observability solutions that increased agent reliability across pharmaceutical environments.
• Designed scalable AI workflows with advanced orchestration that optimized context handling for enterprise data sources.
• Technologies: AI Agents (LangChain, LangGraph, Bedrock, Smolagents, Streamlit); LLM Operations (Tracing, Testing, Evaluation, LangSmith, LangFuse); Infrastructure-As-Code (AWS CDK, Terraform, Typescript, Jenkins); Vectors, Embeddings, RAG (OpenSearch, pgvector, PDF Extraction)

• 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.

• Further development of personalized advertising spaces on the Otto web shop
• Full-stack development in a Kanban-driven team of about 15 people
• Technologies: Microservices, Kotlin, Spring, Spring Boot, Gradle, MongoDB, HTML, JS, Node, SCSS, AWS
• Development process: Kanban; continuous integration with AWS CodePipeline and GitHub Actions