Amazon EC2 Experts

• Development of an agent-based governance platform for the automated assurance of EU AI Act compliance and ODA-compliant orchestration of AI services in complex enterprise environments.

• Design and implementation of an agent-based "Mission Control" framework (Aletheia Conductor) for autonomous state monitoring and process control.

• Development of "Compliance-as-Code" (CaC) solutions based on OPA/Rego for system-wide enforcement of regulatory guardrails.

• Integration of TM Forum ODA standards (TMF630, TMF622, TMF642) to ensure interoperability and standardization.

• Building a highly available event-driven architecture using Redpanda and CloudEvents v1.0 for near-real-time event processing.

• Implementation of an audit-proof "Evidence Chain" through cryptographic linking of trace logs in preparation for automated audits.

• Tech Stack: Java 21 (Quarkus Native), TypeScript (Next.js), Redpanda (Kafka API), CloudEvents v1.0, OPA (Open Policy Agent) & Rego, TimescaleDB, ZincSearch, Redis, TM Forum ODA, Git, GitHub, Clean Code Development, Like-C4.

Project: Defect Generation on Test-Bench Images of Metal Surfaces Environment: Automated Visual Inspection (AVI), Metallurgy & Manufacturing

• Objective & Implementation: Designed, architected, and trained Generative Adversarial Networks (Pix2PixHD / SPADE) for image-to-image transformation. Targeted generation of synthetic material defects (e.g., cracks, inclusions, scale) on rough metal surfaces under real test-bench lighting conditions for privacy-compliant and efficient dataset expansion (data augmentation).
• Technical Design: Implemented robust Generative AI and computer vision pipelines in Python and PyTorch. Used semantic segmentation approaches for mask-controlled defect synthesis and subsequent evaluation with EfficientDet object detection models.
• Business Impact: Massive dataset upscaling (10x) without time-consuming and costly physical test-bench runs, while significantly improving the detection performance of automated inspection systems.

Technologies & Skills Used: Python | PyTorch | SPADE | Pix2PixHD | EfficientDet | Machine Learning | Semantic Segmentation | Computer Vision

• Focus: System architecture, cloud-native platforms, microservices, API design
• Product: Pflege-Pfad is a digital placement platform that directly connects relatives of people in need of care with verified care services and caregivers – without an agency and without ongoing fees.
• Business analysis & process design:
• Analysis of the German care market and identification of the key pain points of both target groups.
• Modeling of core business processes: registration, verification, care request, application, placement, and rating.
• Definition of the business model as a freemium/premium model with optional contact unlocking.
• Creation of user stories and requirements documentation for relatives, care services, and administrators.
• Design of trust and quality assurance mechanisms with document upload, admin review process, and rating system.
• Coordination with stakeholders and validation of product decisions with potential users.
• Technical implementation:
• Design and implementation of the entire platform architecture as the sole developer.
• Design and implementation of a REST API with Spring Boot and Kotlin, including JWT-based authentication.
• Development of the frontend as a single-page application with Angular 17.
• Implementation of the AWS infrastructure with EC2, RDS PostgreSQL, S3, CloudFront, and IAM.
• Document upload with AWS S3 using presigned URLs to verify care services.
• Email notifications via Resend API.
• AI-supported care service search via OpenAI API.
• Implementation of complete user flows such as registration, login, password reset, and the placement process.
• Development of an admin panel for user and care service management as well as analytics.
• CI/CD with GitHub Actions and containerized deployments with Docker.
• End-to-end tests with Playwright.

Technologies: Kotlin, Spring Boot 3, Spring Security, JWT, JPA/Hibernate, PostgreSQL, Angular 17, TypeScript, RxJS, AWS (EC2, ECS, S3, CloudFront CDN, RDS PostgreSQL, IAM), nginx, GitHub Actions, Playwright, Maven, Git, OpenAI API, Resend API, Docker, Scrum, i18n (DE/EN/TR), Kiro, feature flag architecture.

Maturing their API practices on both a business and technology level. My role covers strategy, architecture, developer advocacy as well as hands-on software engineering, enabling both technical teams and business leadership to adopt and act on API-centric principles effectively. Coincidentally, we also establish GitOps, DX and platform best practices with this project.

Highlights:

• Aligning executives with the initiative by clarifying strategy, replacing misconceptions and myths with facts, clarifying the value of existing assets and enabling informed decision-making
• Formulating a way forward for API Lifecycle Management at OTTO
• Driving platform progress and fostering developer engagement by hands-on engineering work towards strategic goals

API Lifecycle Management, Team Topologies, Organizational Evolution, Regulatory, Platform Advocate, Developer Platform, Communities of Practice, Terraform, Kotlin, Kafka, Kong, WSO2, Apigee, Gravitee, Backstage, AsyncAPI, OpenAPI, API Design, AWS, React, Node.js, TypeScript, Redocly, reactive programming, CDC, Golang, Gin, GitOps, DX (developer experience), stakeholder management, roadmaps, workshops, discovery.

Built an international e-commerce platform for a multi-vendor marketplace for digital assets from scratch. Designed and operated cloud native architectures at enterprise scale.

• Designed and operated a highly scalable microservice and serverless architecture
• Built the complete cloud infrastructure with Terraform + AWS CDK in AWS
• Provisioned ECS/EKS clusters (Fargate), Application Load Balancers (reverse proxy), and Lambda functions
• Observability & tracing with CloudWatch, DataDog, Prometheus, and Grafana
• End-to-end setup with DataDog (formerly AWS CloudWatch), Prometheus, and custom Grafana dashboards
• Integration of advanced metrics (including ORM mapper) and distributed tracing with Jaeger
• Robust backup and disaster recovery strategies
• RDS Postgres backups and hourly snapshots
• Read-only, asynchronously synchronized replicas with automated master failover in emergencies
• Minute-level rollback capability through versioned Docker images on ECS and Git-based CI/CD pipelines
• Created CI/CD pipelines with GitHub Actions for automated multi-stage deployments (Dev, Testing, Prod)
• Integrated Stripe for international payment processing
• Built a marketplace payment system with multiple parties and payout routines
• Used Algolia for high-performance real-time search of digital assets on the platform
• Federation of services with GraphQL and Hasura
• Later migration to GraphQL Mesh
• Test Driven Development (TDD) - unit, integration, and E2E testing with Jest, Vitest, and Playwright
• Used Next.js / React for modern frontend applications in the nx monorepo
• Enterprise security architecture & access control
• Integration of JWT tokens with Auth0, OAuth, OIDC, IP guards, BOLA protection, and secret vaults
• Authorization concepts with RBAC, ABAC, and native Postgres Row-Level Security (RLS)
• Built internal microfrontends with Retool for fast prototyping and operational business processes

Technologies: ABAC, AWS CDK, AWS CloudWatch, AWS ECS, AWS EKS, AWS Fargate, AWS RDS, AWS S3, Algolia, Auth0, DataDog, Docker, GitHub Actions, Grafana, GraphQL, GraphQL Mesh, Hasura, JWT, Jaeger, Java, JavaScript, Jest, Kotlin, Kubernetes, Monorepo, Next.js, OIDC, Playwright, Postgres, Postgres RLS, Prometheus, RBAC, Redis, Retool, Serverless, Stripe, Terraform, TypeScript, Vitest

• Worked as freelance Senior DevOps Consultant on-site for IBM at a European Union Agency, operating in a highly secure, air-gapped environment managing classified systems.
• Led automation and DevOps initiatives for a large-scale OpenShift platform (>400 nodes), driving deployment efficiency, GitOps adoption, and operational automation using Ansible, Python, and Bash while ensuring compliance with security requirements.
• Spearheaded automation of release and deployment workflows in a private cloud environment hosting 400+ OpenShift nodes, significantly improving deployment speed and reliability.
• Migrated existing playbooks, roles, and templates from Ansible Tower to Ansible Automation Platform (AAP), ensuring full compliance with fully-qualified collection names (FQCN) and preparing custom Execution Environments (EE) for containerized automation.
• Implemented GitOps Agent for AAP Controller Configuration as Code, enabling automated synchronization (CRUD) of Ansible Controller objects based on repository-stored configuration definitions using GitHub webhooks.
• Designed and automated complex multi-step operational workflows including environment cleanup, Helix cluster component re-creation, Kafka topic management, and OpenShift object lifecycle management across ~100 environments.
• Achieved a reduction of multi-day manual operations to under a few hours through automation improvements spanning multiple AAP clusters and OpenShift environments.
• Integrated Ansible Automation Platform with Thycotic (Delinea) Secret Server via lookup plugin to enhance secure credential management in automated processes.
• Managed deployment tasks, platform troubleshooting, and Istio network configurations while adhering to stringent EU PSC security and compliance standards.
• Collaborated with infrastructure and application teams to refine deployment procedures, develop naming conventions, and continuously improve automation coverage in an air-gapped, classified environment.

• This project involved extraction of intelligence data to support report writing for a company that provides geopolitical, global, commercial intelligence. The data have been gathered from a number of resources (interview transcripts, online data, internal documents), and then a knowledge base has been build from it. This was the basis of a complex RAG system, that was evaluated against a golden dataset. Agents have been used to find out the contradicting intelligence, the statements supporting each other, and to store back the generated knowledge.

Used: Python, RAG, LangGraph, LangChain, deepeval, MCP

• Architected and deployed end-to-end machine learning pipelines across classification and prediction datasets, ensuring robustness and reproducibility through MLOps best practices.
• Contributed directly to LLM model output accuracy improvement by designing and engineering specialised prompts grounded in end-to-end ML and SciML pipeline logic.
• Developed training data for large language models by formulating coding problems that models could not resolve and subsequently documenting the correct solutions.

With project PRIZMA, DB will modernize its infrastructure on the one hand, and develop a fail-safe IT landscape on the other hand, which can be restored quickly and securely in case of a disaster.

• Capture current architectures of existing systems as well as methodical consulting and development of target architectures
• Deepen and maintain the building plan / target IT landscape
• Implement technical architecture concepts & architecture descriptions
• Implement migration concepts for updating and further developing the platform and information systems
• Assess submitted improvement suggestions as part of the project
• Capability management: identify capability gaps, develop target visions, and support transformation planning within the enterprise architecture.
• Create a compatibility matrix of the components in use and compare dependencies of specific versions
• Create an IT concept for extending the platform with the following topics: hardware and software requirements, security, licensing, high availability, load balancing, backup & recovery, update strategy, monitoring integration, etc.
• Coordinate with business architects as well as technical architects from the cross-functional architecture area of the PRISMA program for the topics (backup, Active Directory, monitoring, Citrix, and business applications ...)
• Status meetings and alignment of project planning with the Release Train Engineer / Project Manager
• Advise the Release Train Engineer / Project Manager in identifying project risks
• Advise the System Architect Engineers in steering the implementation of the concept
• Implement the IT concept
• Document the infrastructure

Label: MS Project, LINUX, Windows, ORACLE, Java, REST, SharePoint, Microsoft Exchange, UML, Enterprise Architect, BPMN, AZURE, AWS, V-MODEL, Micro Service, VisualStudio, SAP S/4HANA, SCRUM(SAFE), ESB (TIBCO), Python, Innovator, LeanIX (TOGAF), Ansible, Ansible Tower, Ansible Automation, ROBOT, SpringBoot

• Role: Simulation Engineer
• Environment: Mechanical Engineering / Visual Inspection
• Goals & Implementation: Simulation of photometric-stereo setups to determine the best positions for cameras and light sources for each specific part.
• Business Value: Enabled a low-cost and scalable solution for determining part-specific hardware setups.
• Tech Stack: Python, Blender

Consulting and software for intralogistics automation (AGV/FTS) · Product lines AGVHub (independent AGV project consulting, layout editor) and OneBob (all-in-one business software for small teams)

• Designed and planned the implementation of a supervisory control system for a bakery production line (engineering customer, kick-off workshop 08/2025): three development stages, eight acceptance-ready milestones, batch traceability, AGV integration with RFID, Vue frontend.
• Discovery and requirements discussions with plant logistics and IT teams at industrial customers (life science, aviation, steel, engineering): translated supervisory control, ERP interface, and VDA 5050 requirements into architecture and requirements specification structures.
• Go-to-market automation and analytics: Bing and Meta campaigns, LLM-supported LinkedIn content pipeline, performance dashboards for partners and sales.
• Expert posts on LinkedIn about robotics and intralogistics: around 170,000 impressions across 41 posts (03 to 08/2026).

• Development and optimization of the RTL+ frontend application for SmartTV and set-top box platforms with React and Next.js.
• Key role in the technical coordination of developers within the team and in coordinating implementation.
• Central interface to adjacent teams to simplify development processes and improve cross-team alignment.
• Improved frontend performance, stability, and rendering behavior on low-powered devices in a restricted runtime environment.
• Implemented a frontend testing strategy with Jest, React Testing Library, and Playwright.
• Implemented accessibility improvements according to WCAG 2.2 and WAI-ARIA.
• Integrated Didomi Consent Management as a contribution to increasing ad monetization on streaming platforms.
• Used AI-supported engineering workflows with Cursor for structured implementation, refactoring, and faster problem solving.

Technologies used: React, Next.js, TypeScript, JavaScript, GraphQL, Apollo Gateway, Zustand, Tailwind CSS, Styled Components, React Testing Library, Playwright, Jest, HTML5, CSS3, AWS Lambda, EC2, CloudFront, S3, GitLab CI/CD, NX, Cursor

IT consulting, analysis, architecture design, new and further development, code review, test automation, continuous integration, continuous delivery in backend and frontend areas for Automotive Project Instavalo.

Frontend:

• Implementation of UI components according to specifications, especially style guides and responsive design eith React and Typescript
• Component testing
• Code documentation
• CI/CD with Gitlab Pipeline

Backend / IoT:

• Analysis and architectural design with AWS Greengrass IoT on Edge Devices
• Setting up Microservices containers with Docker Compose on Edge device with AWS Greengrass and AWS IoT IAM, Token Exchange Service, Ansible
• CI/CD with Gitlab Pipeline, Terraform, AWS ECR
• Logging with Fluentbit Lua Language for AWS Cloudwatch
• Python Lambda for AWS Greengrass Recipe deployment on Edge Devices
• Implementation of test-driven development with JUnit, Mockito, and code Coverage
• Jacoco
• Definition of REST interfaces with OpenAPI / Swagger
• Development and enhancement of software based on Java Quarkus, Typescript NestJs NodeJs and Python
• Authentication and authorization in Aws IAM
• Development of REST and gRPC interfaces for the frontend and backend
• Implementation of Maven dependencies with DevSecOps OWASP
• Spring AI, Jetbrains AI Assistant, Junie, Github Copilot, Claude Code, Agents, Skills, Command, Hooks, Subagents

Designed and developed an Azure-based AI and data platform transforming distributed project documents into searchable, structured knowledge for an enterprise AI assistant. Combined incremental document processing, hybrid keyword/vector retrieval, knowledge graph extraction, and conversational AI for Microsoft Teams and Microsoft 365 Copilot. Implemented Python document-processing pipelines for PDF, Word, Outlook and text files; Azure Cosmos DB lifecycle management; Azure AI Search; Azure AI Foundry and Azure OpenAI; identity-aware retrieval with Microsoft Entra ID; project authorization; a TypeScript Microsoft Agents SDK agent; Pulumi infrastructure; resilient scheduling, retry and diagnostic tooling. Technology stack: Python, TypeScript, FastAPI, Pydantic, Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure Cosmos DB, Blob/Table Storage, Container Apps/Jobs, Service Bus, Key Vault, Entra ID, Docker, Pulumi, pytest, Microsoft Teams and Microsoft 365 Copilot.