Python Experts

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.

Project: Defect generation on inspection images of metal surfaces

• Environment:* Automated Visual Inspection (AVI), Metallurgy & Manufacturing

• Goal & implementation: Concept, architecture, and training of 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 inspection-light conditions for privacy-compliant and efficient dataset expansion (Data Augmentation).

• Technical design: Implementation of robust Generative AI and Computer Vision pipelines in Python and PyTorch. Use of semantic segmentation approaches for mask-guided defect synthesis and downstream evaluation with EfficientDet object detection models.

• Business impact: Massive dataset upscaling (factor of 10x) without time- and cost-intensive physical inspection runs, while at the same time drastically improving the detection performance of automated inspection systems.

Technologies & skills used: Python | PyTorch | SPADE | Pix2PixHD | EfficientDet | Machine Learning | Semantic Segmentation | Computer Vision

Reporting and controlling with Power BI for a productive ERP system

• Analysis of ERP data and interfaces for use in Power BI dashboards
• Evaluation and migration of existing reports (e.g. Excel) to Power BI
• Development of an access rights concept for selective data access
• Documentation and training on how to use and adapt the Power BI dashboards

Label: Power BI, Excel, SelectLine ERP, Microsoft SQL, SQL Server Management Studio

Creation of concepts for M365 implementation, especially Tenants, EntraID, EntraConnect and ExchangeOnline, taking BAS standards into account (mandatory baseline security requirements) in the project "Concept M365" with the aim of transferring the concepts to the M365 environments of Bitmarck and then handing them over to the customer.

• Creation of a current-state analysis of the existing M365 environments as well as the on-premises environments and the BAS standards.
• Creation of concepts for the topics Tenants, EntraID, EntraConnect and ExchangeOnline taking the BAS standards into account
• Design and implementation of an automated solution for creating standardized M365 tenants based on Microsoft M365 DSC (Desired State Configuration)
• Transfer of the concepts into the M365 environments
• Creation of detailed technical documentation

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

• Planned and developed a CPU monitoring application for monitoring system performance and resource utilization.
• Designed and implemented a Spring Boot backend providing a REST API for processing and exposing monitoring data.
• Developed the React frontend for presenting monitoring information in a clear and user-friendly interface.
• Integrated PostgreSQL for persistent storage and management of application data.
• Containerized the application and its services using Docker Compose.
• Automated infrastructure provisioning and deployment using Terraform on AWS.
• Structured the application as a modern, maintainable system using REST-based communication between frontend and backend.
• Designed and developed a secure, scalable CPU monitoring architecture (cpu-watcher) with a dedicated collector application that streams monitoring data to the backend, reducing direct exposure of system resources.
• Designed a secure cloud infrastructure with the database isolated within a private network and OIDC-based authentication.
• Implemented Infrastructure as Code with Terraform and integrated version-controlled CI/CD pipelines to automate testing, infrastructure changes, and application deployments.
• Designed and implemented the frontend delivery architecture using AWS CloudFront.

Stack: Spring Boot · React · PostgreSQL · REST API · Docker Compose · Terraform · AWS

Stabilizing an Azure/.NET landscape in live operation.

• Architecture, DevOps, and operational readiness; technical decisions under time pressure
• Azure DevOps, monitoring, ETL/ELT, cloud security, FinOps, and data-mesh-related topics

Technologies: Azure DevOps, .NET, CI/CD, monitoring, FinOps

• 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.

• Marketing Strategy: Lead the end-to-end analysis and evaluation of cross-channel marketing campaigns across the entire Customer Journey. My focus is identifying optimization potential and deriving clear, actionable recommendations that drive measurable business impact.
• Data Science & AI: Advanced predictive modeling (Churn, LTV), market basket analysis, clustering, and real-time AI-powered audience discovery utilizing RAG/LLMs.
• Marketing Analytics & Measurement: End-to-end attribution analysis, Marketing Mix Modeling (MMM), audience segmentation, conversion path analysis, and A/B testing across all major platforms.
• Data Engineering & Reporting: Designing and managing robust, multi-platform data pipelines (BigQuery, GCP) for data consolidation, automated dashboard generation, and critical API integrations.

• Development of a production-ready Enterprise AI solution for the automated processing of invoices and business documents
• Integration of Azure AI Document Intelligence and LLM technologies into existing business processes
• Development of robust REST APIs for automated document processing and system integration
• Extraction, validation, and storage of structured invoice data in Azure SQL as a base for analytics and machine learning models
• Development of an AI-based recommendation engine with machine learning and deep learning to generate personalized product recommendations based on historical purchase data
• Implementation of logging, monitoring, error handling, and validation mechanisms for stable production use
• Collaboration with business teams to define business rules and integrate the solution into existing enterprise processes

Technologies: Python, Azure AI Document Intelligence, Azure OpenAI, Azure SQL Database, REST APIs, Machine Learning, Deep Learning, OCR, Pandas, JSON, Workflow Automation

• Planning and concept development for building a new business model, including structuring and implementing business processes.
• Coordination and management of external service providers as well as interface management.
• Introduction and implementation of new digital tools and processes to optimize operational workflows.

• Development of an AI-powered project organization tool for a civil engineering company that intelligently links project, task, tender, schedule, and document data through a knowledge graph.
• Implementation of AI features for document analysis, information extraction, context-based assistance, and voice-based data capture based on Microsoft Azure AI, reducing administrative effort, making information available faster, and supporting project teams in decision-making.
• Tech stack: Python, React, TypeScript, FastAPI, Claude Code, Codex, Graphify, PostgreSQL, Microsoft Azure AI Foundry, Azure OpenAI, Azure AI Speech, Azure AI Document Intelligence, Microsoft Graph, Microsoft Entra ID, Docker, Git, CI/CD.

Product:

• Built a production-ready AI platform for generating brand-aware marketing images and videos from product data, user requirements, and uploaded media.
• Own the platform architecture, technical roadmap, API design, security, deployment workflow, operational reliability, and model-provider strategy.
• Developed the core platform in .NET and built supporting AI and workflow prototypes in Python, applying language-independent API contracts and structured interfaces between services and model providers.
• Implemented reliable background processing with RabbitMQ, persisted workflow state, idempotent handling, retries, failure recovery, logging, secure storage, authorization, and credit accounting.
• Made pragmatic build-versus-buy and model-routing decisions based on reliability, latency, cost, and maintainability rather than novelty.

Agent Orchestration & RAG Systems

• Built and compared agent workflows using Microsoft Agent Framework, LangGraph, and LangChain, including tool use, conditional routing, clarification steps, state management, and hand-offs between agents.
• Implemented reusable .NET components for agents, prompts, tools, model providers, structured responses, and retrieval with pyvector, making it easier to change AI providers without rewriting the core workflow.

• Led the hardware development and validation of a safety-critical 400 V battery management system (BMS) for the TOGG SUV program; performed system architecture reviews, schematic validations, EMC and reliability tests, and root cause analyses in line with relevant automotive standards (ECE-R10, CISPR25, ISO-26262). Coordinated cross-border EU engineering teams, customer reviews, and technical documentation to deliver a production-ready, validated system.

• Architected and integrated 22 state-of-the-art ADAS validation vehicles for BMW ADCAM and LIDAR programs at Magna Electronics; synchronized up to 26 heterogeneous sensors (LIDAR, RADAR, cameras, GNSS/INS) using PTP-based timing architectures. Defined the system architecture, HW/SW interfaces for hardware-in-the-loop (HIL) reprocessing, sensor integration strategies, and data acquisition frameworks. This enabled scalable vehicle-level validation, improved validation efficiency by 25%, and reduced project costs by €1.45 million.

• End-to-end validation and integration of automotive radar platforms for a Daimler project at Continental. Developed automated open-loop hardware-in-the-loop (HIL) environments to accurately test target tracking KPIs, field of view limits, and thermal and voltage-related ECU state machines. Used a highly complex toolchain consisting of CANoe, Lauterbach Trace32, RADAR target simulators, and EMC shielding chambers for RF and system validation. Synchronized global, interdisciplinary teams to speed up troubleshooting and close critical technical gaps.

• Reconstructed and validated the product architecture of an electromechanical e-bike by integrating and troubleshooting critical subsystems (BMS, motor control, sensors, HMI, electronic locking systems). Built a comprehensive system-level test bench for functional testing, fault reproduction, and performance analysis; coordinated suppliers and implemented corrective actions to improve reliability and traceability.

• Defined the system architecture and validation strategy for a LIDAR platform developed in cooperation with Elmos Semiconductor; evaluated optical measurement concepts, SPAD detector integration, and system requirements, and created technical recommendations for product development and verification.

• Developed LabVIEW-based automation and verification software for a high-precision hydraulic and electromechanical test system for FTE Automotive; integrated NI DAQ hardware for high-frequency real-time capture of physical measurement data. Developed automated test sequences, programmable endurance tests, troubleshooting routines, data logging, and analysis tools to improve test efficiency and traceability.

• Successfully managed complete engineering life cycles for well-known industrial customers (Magna, Continental, Farasis, FTE Automotive, BMW, Daimler, TOGG) – from requirements engineering and proof of concept through system integration and validation to technical documentation, supplier coordination, and user training.