NGINX Experts in Germany

Developed a CPU monitoring application using Spring Boot and React, containerized with Docker Compose, with automated infrastructure provisioning using Terraform on AWS.

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

• Building a data integration platform for high, medium, and low voltage assets for contextual analysis of time series with master data from the SCADA control system (IEC 60870 104), INIS, and SAP.
• Responsibility for the architecture and implementation of the solution, as well as sparring partner for the Product Owner.
• Use of Kotlin, Spring Boot, Maven, TimescaleDB, PostgreSQL, liquibase, Elements IoT, Docker, Kubernetes, Grafana, Python, jupyter, and various API gateways.

• Architected the Yatta Integration Layer – a config-driven integration platform on Java 25, Spring Boot 4 (WebFlux), Temporal, gRPC and Kafka, enabling new third-party integrations (e.g. AVS fulfillment) via declarative JSON configs with zero code changes.
• Designed and implemented Tink integration with 0Auth IBAN verification to enhance fraud prevention and account validation workflows with Adyen payByBank.
• Architected and implemented an OpenFGA-based authorization model for centralized management of users, groups, and fine-grained access control in the vendor portal.
• Architected and led delivery of the Yatta API Gateway platform using GraphQL Federation, providing a unified enterprise API layer across distributed microservices with centralized authentication, authorization and request orchestration.
• Replaced NGINX + NLB with Istio service mesh and AWS ALB; rolled out WAF, OAuth (Cognito), IP whitelisting and RBAC across environments.
• Migrated CDC from Confluent Cloud connectors to a self-hosted Kafka Connect + Debezium stack, reducing operational cost by ~80% across multiple environments.
• Implemented the Transactional Outbox pattern with Debezium for reliable, exactly-once event publishing to Kafka with Avro and Schema Registry.
• Migrated dunning/payment-recovery workflows from Airflow to Temporal, achieving 99.9% reliability for settlement handling.
• Optimised Apache Airflow with deferrable sensors to handle 1000+ concurrent DAG runs without scaling the worker pool.
• Refactored a monolithic Terraform codebase into 3 modular projects, cutting deployment time by ~45%.
• Stood up full observability with OpenTelemetry, Tempo, Prometheus and Loki; automated dev/staging/prod with ArgoCD, Image Updater and Helm.
• Collaborated with product, operations and engineering stakeholders to define scalable platform architecture and integration standards aligned with long-term business and operational goals.

• Short description: Leading a group-wide project to establish standardized cloud governance for Microsoft Azure, including policies, security and compliance controls, automation, and cost and operations management while preserving the autonomy of decentralized business units within regulatory frameworks.

• Tasks and activities:

• Overall responsibility for designing, building, and implementing a company-wide cloud governance structure (Azure), including target picture, roadmap, and operating model.

• Managing internal and external stakeholders (C-level, IT, Security, Compliance, Cloud Architecture, DevOps), including decision and escalation management.

• Planning and facilitating workshops on cloud strategy, governance principles, and the design of areas such as identity, connectivity, and platform management.

• Defining, implementing, and rolling out cloud policies (Azure Policy / custom policies), security standards, and compliance requirements (including GDPR, ISO 27001, BSI C5).

• Building a cloud governance framework based on the Azure Cloud Adoption Framework (CAF), including landing zone and guardrail concepts.

• Introducing automation solutions for governance, security, and cost control (policy/control automation, IaC, CI/CD-based control mechanisms).

• Implementing cloud security and compliance monitoring mechanisms as well as continuous improvement processes.

• Establishing and operationalizing FinOps in an enterprise environment (central and decentralized FinOps teams), including cost management strategies, reporting, and guardrails.

• Integrating governance policies into DevOps processes (e.g. CI/CD principles for security and compliance checks, GitLab Runner concept in spokes, GitLab CI/CD for CAF landing zones).

• Implementing access concepts including RBAC design and breaking-glass mechanisms (emergency access) as well as certificate automation (ACME / step-ca).

• Achievements:

• Created a unified, auditable governance and control set for Azure (policies, standards, compliance mapping) and thus laid the foundation for scalable cloud use in a regulated environment.

• Established repeatable automation for governance, security, and cost control (IaC + CI/CD), reducing manual effort and implementation risk.

• Improved operational and decision-making capability across central and decentralized units (clearer roles, responsibilities, escalation paths, balance between autonomy and group requirements).

• Significantly increased workload compliance during lift-and-shift migrations.

• Technologies used:

• Microsoft Azure Policy, custom policies.

• Terraform, OpenTofu, Terragrunt.

• step-ca (ACME).

• Entra ID.

• Azure Firewall.

• Azure Networking, hub-and-spoke architecture.

• Azure vWAN (evaluation).

• Azure Front Door, Azure Application Gateway.

• Azure ExpressRoute.

• Azure Key Vault.

• NetBox.

• GitLab (on-premises).

• Infrastructure, concepts used:

• Cloud shared responsibility model.

• Hub-and-spoke connectivity / central shared services (from hub-spoke context).

• Central governance with decentralized delivery (business unit autonomy with guardrails).

• Methods used:

• Scrum.

• Stakeholder management (C-level to engineering).

• Cloud governance, Azure Cloud Adoption Framework (CAF).

• DevOps, CI/CD.

• Cost and FinOps approaches: tagging/chargeback models, budget/alert concepts, reserved instances/savings plans vs. on-demand scenarios, sensitivity analyses.

• RBAC, breaking-glass concepts.

• ACME / certificate automation.

• GitLab Runner concept in spokes, GitLab CI/CD pipelines for CAF landing zones.

• Focus: system architecture, cloud-native platforms, microservices, API design
• Product: Pflege-Pfad is a digital matchmaking platform that connects relatives of people in need of care directly 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 the 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 a solo 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 via presigned URLs for verification of 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 placement process.
• Building 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.

• Designing the audit logging framework
• Implementing APIs for developers to integrate in their codebase
• Implementing ingestion pipeline, database query layer and UI for browsing the audit events
• Improving stability and reliability of the backend system

As project manager for the Odoo implementation at a small company, I was responsible for designing, implementing, and training a fully integrated CRM and accounting system. Through structured requirements analysis, precise data migration, and targeted change management, I was able to complete the rollout in just eight weeks. The project led to a significant reduction in manual tasks, faster business processes, and increased real-time transparency.

Main Responsibilities

Requirements analysis and process mapping Configuration of Odoo modules: CRM, Sales, and Accounting Data migration (Excel/CSV → Odoo) and quality control Creation of workflows, automated email rules, and dashboards Conducting training sessions and providing support after go-live Project coordination (budget, schedule, stakeholder communication)

Key Achievements

Full implementation of the Odoo suite within the set timeframe (8 weeks) 30% reduction in accounting time and 25% acceleration of the lead-to-sale flow 100% customer satisfaction after go-live, based on survey results Successful migration of 100% of existing master data without data loss Establishment of a sustainable support infrastructure (3-month post go-live support)

Impact

Improved decision-making through real-time dashboards and automated reports Increased efficiency and cost savings (≈ €8,000/month) Scalability for future growth (additional modules can be integrated seamlessly) Strengthened sales and finance departments through seamless process integration

This summary highlights how I created concrete and measurable value for the company through structured project work, technical expertise, and targeted training.

• Development and introduction of operational processes
• Preparation of complete documentation packages (including emergency management and operations) to meet compliance requirements
• Introduction of a workshop on IaC (Infrastructure as Code)
• Professional consulting for the project's security concept (ISMS)
• Installation and operation of Kubernetes clusters on AWS, on-prem, and Azure
• Design of hybrid cloud architecture (on-prem, Hetzner, AWS)
• Analysis and resolution of incidents and system outages
• Network changes to firewall rules, gateways, OpenVPN settings, and IPsec tunnel (pfSense)
• Professional consulting on BitBucket, Jenkins, and GitLab CI/CD pipelines
• Consulting on Ansible deployments and infrastructure automation
• Consulting on building a scalable system in the cloud (AWS / Azure)
• Technologies / Tools: Ansible, Terraform, AWS, Azure, VPN, pfSense, Jenkins, Bitbucket, Kubernetes, GitLab Runner, ISMS, Golang, Prometheus, Grafana, S3, Lambda, RDS, ECS, Cognito, OIDC, Harbor, MinIO, Postgres, Redis, Keycloak, Ceph, Proxmox, CloudFormation, PostgreSQL, Flux CD, Hetzner, IONOS, Sonatype Nexus Repository, Entra ID, Dex IdP, Pulumi

• Designed, built, and launched an internal event planning web application used by over 100 department leads for a large event, despite having no dedicated testing phase.

• Ensured smooth, failure-free operation during first production use, leading to the tool being adopted for future events.

• Automated catering calculations and related workflows, significantly reducing email communication and manual computation effort for meal planning.

• Managed deployment and hosting on a Linux server using Coolify, including application setup and runtime operations.

• Hired and guided a communication designer on UX while independently owning all technical decisions and implementation.

• Understand the customer’s business & technical requirements and translate them into system / technical requirements
• Design and implement Schrodinger’s applications on cloud systems, and experience convincing senior management and senior technical staff of the benefits of their journey with Schrodinger on the cloud
• Provide exceptional technical design and thought leadership, especially around AWS, GCP, and K8s architecture reviews, performance, high availability, cost, and security
• Deep understanding of the Well-Architected pillars and all best practices for building a secure, performant Schrodinger’s applications on the cloud platforms
• Lead technical workshops and advise customers on architectural and strategic IT decisions
• Ensure success in designing, building and migrating applications, software, and services on the cloud platforms
• Educate customers on best practices to ensure their solutions are designed for successful deployment in the cloud
• Work with other team members to ensure quality and customer success
• Define the tickets, tasks, and timelines of projects
• Collaborate with account managers to ensure that the projects are executed according to the defined plan and timeline
• Monitor the progress of the projects, identify risks and issues, and take proactive measures to mitigate them
• Lead and inspire cloud architect teams, provide guidance, and make critical decisions
• Facilitate effective communication and collaboration among team members
• Collaborate with Schrodinger’s managers to improve deployment, support, and configuration of Schrodinger’s applications
• Lead weekly standups and define priorities

• Development and maintenance of a news portal CMS with interfaces.
• PHP development with HTML, CSS, SCSS, JavaScript, MySQL, Laravel, documentation of the interfaces and the system.
• Further development of the SSO system based on Laravel with a large API and connections to AWS, SAP, ERPS.
• Reorganisation and development of the news portals on the basis of a publish platform, services and interfaces to Google, Facebook, Twitter, SSO, SAP technical based on NodeJS with TypeScript and AWS.
• Technologies: PHP, Laravel, MySQL, JavaScript, Vue.js, NodeJS, TypeScript, jQuery, CSS, SCSS, SASS, Unix, Jira, Confluence, Git, Apache, Nginx, AWS.

• Expert-supervised AI systems for scientific reasoning, model evaluation, and research workflows.
• Built the IAS Problem Solver, an orchestrated system for difficult mathematical reasoning; it achieved 84% in one submitted answer set on the Leipzig mathematics benchmark.
• Built a resumable state-machine pipeline for research-grade mathematics benchmark generation: source selection, LLM-agent-based phenomenon discovery, task synthesis, gold-answer and certificate generation and validation, probing, repair, human feedback, and quality gates, targeting tasks that are difficult, natural, verifiable, and cost-effective.
• Current work extends this into budget-aware AI research workflows for real scientific problems with expert review.

Tech stack: Python, OpenAI/OpenRouter-compatible APIs, embeddings, RAG, SQLite.

• Analysis of requirements and the current situation
• Migration of an old domain structure and Tobit to Exchange 2019 with integration to MS365
• Evaluation of implementation paths and planning for securing sensitive data
• Planning regarding BSI basic protection, the German Federal Data Protection Act (BDSG), and GDPR
• IT governance and IT security backtests
• Support with ERP setup and securing mail transfer
• Hyper-V 2016/2022, Microsoft Terminal Services Cluster, Microsoft Exchange 2019
• Office 365, Microsoft 365, Azure AD, Teams, Salesforce
• Cisco, Zyxel, and HP switches, Sophos firewalls/UTMs, SecurePoint
• Microsoft IIS 2022, IPv4/IPv6, Veeam, Wortmann online backup, NextCloud
• Services: DNS, DHCP, TCP/IP, subnetting, firewalling, routing, VoIP, Group Policy (GPO), SIEM, remote work, home office, high availability, failover, VLAN, PRTG

• 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).