Spring Cloud Experts in Germany

• Fully on-premises LLM document-examination platform for a compliance-critical banking domain: agentic LangGraph pipeline with deterministic verification, every AI judgment structured and source-anchored; ~960 automated tests, zero data egress
• GPU throughput engineering (quantized serving, speculative decoding, prefix caching): 9.5x extraction speed-up, 500+ multi-document case files per day on a single A100
• AI-native EDI/EDIFACT integration platform (~116k LOC Java 25 / Spring Boot 4, 1,900+ tests): LLM-drafted partner mappings machine-verified before go-live (DFDL conformance, field-coverage checks, dry runs), ~99.5% byte match on real customer files — replacing weeks of manual mapping per partner

Development and further development of an internal platform for managing and providing technical resources, virtual machines, and infrastructure services. The platform supports self-service processes and covers functions that are conceptually comparable to cloud management solutions like Azure or AWS.

• Responsible involvement in the design, development, and implementation of new backend and frontend features
• Hands-on development with Java, Spring Boot, Python, and Angular
• Implementation of REST interfaces, business logic, validations, and integrations into existing system landscapes
• Further development of modern web interfaces with Angular, including connection to backend services
• Participation in architecture and design decisions within the team, especially with regard to scalability, maintainability, and clean interfaces
• Containerization and deployment of applications with Docker, Kubernetes, and Helm
• Support with CI/CD processes and deployment to Kubernetes-based environments
• Work in the environment of vSphere, Broadcom, GitLab CI/CD, ArgoCD, Maven, npm, and NuGet
• Close collaboration with developers, business teams, DevOps, and other technical stakeholders
• Analysis of technical requirements, deriving suitable solutions, and independent implementation in an agile team
• Use of GitHub Copilot to support code generation, refactoring, test case creation, and technical documentation

Methods/ tools/ technologies: Languages & frameworks: Java (21), Spring Boot (4.x), Python, Angular, Robot Framework, Kubernetes, Helm Persistence: PostgreSQL, MongoDB, Hibernate, Liquibase Architecture & communication: REST, gRPC, GraphQL, Apache Kafka, OpenAPI, Microservices, Event Driven, Domain Driven Design Cloud & infrastructure: Terraform, Docker, Rancher, Helm, Ansible Security: OAuth2, MS (Entra ID), web security, Keycloak (extensions for detailed group rights) DevOps: GitLab CI/CD, Ansible, Maven, Gradle, Grafana, Prometheus, Git, GitHub Copilot Testing & QM: JUnit, Robot Framework, automated component and integration tests, E2E tests with Playwright, Testcontainers, EasyMock Methodology & approach: Kanban, JIRA, Confluence, Clean Code

• Creation, maintenance and hardening of Kubernetes clusters employing Ansible and ArgoCD
• Keywords: Ansible, AWX, Kubernetes, NetApp, Prometheus, CI/CD ArgoCD, SSO, Fluent-bit, HAProxy, Calico, Keycloak, oauth2-proxy, SealedSecrets, kubeseal, Aqua kube-bench, CIS-Benchmarks, Aqua Trivy operator

• 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).

• Client: UOB Bank

• Projects: Web Server Management; Online Account Opening; Online Account Lending

• Develop and support enterprise-grade backend services for banking/insurance workflows using Java 17 and Spring Boot.

• Design REST APIs, platform integrations and backend services for customer onboarding, account workflows and lending journeys.

• Modernize legacy services into scalable microservices while improving reliability, maintainability, CI/CD and production delivery.

• Optimize PostgreSQL/MongoDB data access patterns and collaborate with Agile teams through reviews and technical guidance.

• Environment: Java 17, Spring Boot, REST APIs, Microservices, PostgreSQL, MongoDB, Docker, Jenkins, GitLab, SonarQube, Agile

• Led development of 15+ microservices (Java 17, Spring Boot) driving customer interactions; migrated from on-prem to GCP Kubernetes, improving scalability and reducing infra cost by 20%.
• Optimized user services with CouchDB caching and API refactoring, cutting response times by 35% and enhancing customer experience.
• Implemented canary deployments, FluxCD GitOps, and CI/CD optimizations in GitLab, reducing release lead time by 25% and enabling zero-downtime rollouts.
• Set up Grafana health checks and Anodot alerts for latency, error, and throughput monitoring, reducing MTTR by 40%.
• Built secure APIs using OAuth2, DPoP, and Gatekeeper, integrated REST and GraphQL, and achieved 90%+ test coverage with unit and E2E tests.
• Mentored junior developers, promoted Agile best practices, and collaborated cross-functionally to deliver high-impact, reliable customer-facing features.

• Architected, developed, and maintained scalable backend services using modern Java (17/21), Kotlin, and Spring Boot for high-traffic, performance-critical platforms.
• Served as a technical leader for complex initiatives, taking features from inception through deployment, and implementing runtime validation layers using advanced LLM APIs with secure guardrails.
• Optimized PostgreSQL schemas and query efficiency at scale, drastically reducing data retrieval latencies and ensuring cross-service transactional consistency.
• Spearheaded continuous improvement in development processes, designing comprehensive unit and integration testing frameworks that drove test coverage from 50% to over 90% via JUnit, Spock, and Mockito.
• Partnered extensively with cross-functional product and data squads to define API boundaries, design interfaces, and ensure rapid, high-confidence CI/CD release cycles.

• Frontend development
• Spring Boot
• Swagger
• Git
• GitLab
• SonarQube
• Docker
• Maven
• Java
• Angular

• Contributed to the development of an advanced RAG AI Chat application that integrates multiple LLM models, enabling users to seamlessly switch between models based on specific tasks. This improved the user experience by providing tailored, efficient solutions for various use cases, such as event scheduling, booking systems, and complex task management.
• Participated in designing and implementing a robust backend architecture using Spring AI, enabling advanced AI-driven capabilities like intelligent task automation, language processing, and contextual recommendations. Leveraged Spring AI Tools and Advisors to enhance the performance and decision-making of the AI models.
• Collaborated on the integration of vector databases to support embeddings, enhancing the app's ability to understand user queries and perform actions based on complex, real-time data inputs.
• Contributed to the development of a seamless, user-friendly front-end interface using React with TypeScript support, ensuring a modern, responsive, and scalable user experience across platforms.
• Assisted in implementing state management with Redux RTK for efficient data flow and real-time updates, optimizing the overall user experience in dynamic scenarios such as scheduling and task management.
• Partnered with stakeholders to define feature requirements, helping ensure that the app could scale to meet evolving business needs and integrate with other systems like calendar and email services. Worked alongside cross-functional teams, including data scientists and UI/UX designers, to fine-tune AI models and ensure alignment with project goals.
• Contributed to ensuring end-to-end system performance, security, and compliance by helping integrate authentication mechanisms, role-based access control, and secure communication protocols in both backend and frontend layers.

Technology Stack and Key Contributions:

• Java / Spring Boot / Spring AI: Developed backend services leveraging Spring AI for intelligent responses, task automation, and complex workflows.
• React / TypeScript: Built intuitive user interfaces with React and TypeScript, ensuring a smooth and scalable frontend.
• Redux RTK: Managed application state with Redux RTK for optimized state management, enabling dynamic, real-time data updates.
• Vector Databases: Integrated vector databases (pgVector) for embedding support, improving AI model performance in handling complex queries.
• PostgreSQL / Redis: Managed persistent and temporary data with relational and in-memory data stores, ensuring data integrity and speed.
• Kafka: Utilized Apache Kafka for event-driven communication and seamless integration between microservices.
• Spring Security: Ensured the security of backend services with robust authentication and authorization mechanisms.
• CI/CD & DevOps: Integrated continuous integration and deployment pipelines to ensure rapid and secure deployment of features and updates.

• Technical leadership and architecture for enterprise clients (including LR Health & Beauty, Deutsche Bahn, Trusted Shops)
• Cloud migration, microservices architectures, CI/CD optimization

• Further development and maintenance of a complex sales platform with a focus on digital closing processes, customer portal interactions, and document generation for financial and insurance products
• Development and maintenance of microservices with Spring Boot 3 and Kotlin
• Frontend development with Angular 20 for displaying products, applications, and documents
• Execution of end-to-end tests with Playwright and integration tests with WireMock
• Creation and optimization of Quartz jobs and CronJobs
• Refactoring of the security configuration in a multi-realm Keycloak setup with a custom filter chain, permission evaluator, and factory routing
• Creation of dynamic emails with Thymeleaf
• Extensive error analysis with Application Insights, Log Analytics, and direct SQL debugging
• Introduction of a dynamically controlled security architecture with flexible token handling depending on path and realm
• Stabilization and clear structuring of the closing process for complex product models
• Significantly improved maintainability and readability of the code through modular refactorings
• Higher test stability and depth through combined use of Playwright, WireMock, and integration tests
• Targeted performance optimization through analysis of Hibernate statistics, query tuning, and use of SQL execution plans
• Technologies used: Java 21, Kotlin, Spring Boot 3, Angular, TypeScript, REST API, JSON, Kubernetes, Docker, PostgreSQL, Liquibase, Keycloak, OAuth2, GitHub Actions, Testcontainers, Azure Application Insights, Thymeleaf, Tilt, Microsoft SQL Server

• Analysis
• Consulting
• Software design
• Development
• Automation
• Testing
• Deployment
• Architecture, development and deployment of various proof-of-concept applications around the integration of current AI interfaces including conversational, realtime voice, images and videos
• Developed best practices for working with agentic systems and AI in practice
• Created code templates

• Developed backend features for Modern RIS, a web-based Radiology Information System integrated with the existing Classic RIS via WebView.
• Worked on a modular Spring Boot backend covering clinical workflows such as appointments, examinations, patients, orders, reporting, billing, and inventory.
• Contributed to event-driven architecture using domain events to decouple workflows across backend modules.
• Implemented REST/OpenAPI endpoints, service-layer business logic, DTO mapping, validation, and integration points for the React frontend.
• Worked with PostgreSQL-backed domain models, Liquibase database changes, read/write model separation, and legacy RIS database structures.
• Integrated authentication and authorization flows using Keycloak and OAuth2.
• Added and maintained unit/integration tests using JUnit, Rest Assured, Testcontainers, and project-specific test utilities.
• Supported CI/CD and local development workflows using Maven, Docker Compose, Jenkins, and generated OpenAPI clients.

Tech stack: Java 21, Spring Boot 3.5, Maven, PostgreSQL, Liquibase, Keycloak, OAuth2, REST, OpenAPI/Springdoc, MapStruct, Lombok, Docker, Testcontainers, Jenkins.

• Mobile app backend development for end-customer mobile management
• Implementation of authentication methods
• Conversion of a monolith into microservices
• Operational support
• Technologies: Kotlin, Quarkus, Microprofile, Jackson, MariaDB, Redis, JSON, XML, SOAP, Maven, Git, GitLab CI, Splunk, Grafana, Jenkins, Kubernetes, Helm, Terraform, Microservices