MongoDB Experts in Germany

• Task Engineering: Evaluating Coding Agents.
• Asset Creation: Building realistic task environments using datasets and files in German. Crucially, these assets must remain in the target language to genuinely measure multilingual handling.
• Prompting & Translation: finding failure points where AI does not work, in German.
• Implementation & Verification: Supporting the development of robust solutions (reference implementations) and write highly reliable, deterministic verifier scripts (using rubric-based judging only when strictly necessary).
• Calibration & Execution: Analyze execution logs and calibrate task difficulty (Easy to Very Hard) using standard Terminal-Bench run configurations against various model tiers (Haiku, Opus).
• Quality Assurance: Participation in a rigorous, 4-layer human quality control process (creation, human review, calibration review, and audit) alongside automated LLM-based checks to ensure fairness, grammatical accuracy, and benchmark integrity.
• Linguistic Review: Reviewing AI benchmark tasks across Hindi, Arabic, Japanese, Chinese, Czech and Turkish.

• Optimizing and accelera­tion of our Gitlab CI pipeline

• Conceptual work for the PoC of the Zero Trust system

• Extension of the policy-engine backend in Go

• Extension of the policy-testing mechanism in Python

• Architectural design of the PEP component of Zero Trust

• Documentation of the product

• Technologies: Zero Trust, Go, Python, Gitlab CI, Docker, JWT, Domain-Driven Design

• 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.

• Developed cloud-native enterprise applications on SAP Business Technology Platform using Node.js, SAP UI5, and RESTful APIs, delivering solutions across training management, procurement, employee information, and logistics domains
• Served as the primary developer for the maintenance, enhancement, and production support of three enterprise applications, delivering new features, resolving production issues, and coordinating releases with business stakeholders
• Experienced in leveraging AI-assisted development tools such as Microsoft Copilot to accelerate feature development, generate code, prototype solutions, and support application migration and modernization
• Designed backend services, domain models, and SAP Fiori/UI5 interfaces, implementing business workflows, role-based access control, validations, scheduling, reporting, and data import/export capabilities
• Designed and integrated enterprise services with SAP SuccessFactors, SailPoint, ERP systems, and external Learning Management APIs, including automated synchronization for 11,000+ user data
• Designed and implemented AMQP-based event-driven services processing up to 500 RFID parcel scan events per day for a logistics application
• Managed deployments and application operations using CI/CD pipelines, SAP Solution Manager, SAP BTP Cockpit, Kibana, and cloud monitoring tools, performing root-cause analysis and resolving production incidents
• Managed application dependencies by resolving npm package version conflicts and remediating critical and high-severity security vulnerabilities, ensuring production compliance and application stability
• Collaborated with architects, business users, SAP governance teams, and distributed Agile teams throughout technical design, code reviews, sprint planning, documentation, and software delivery

• LLM-based features for automated CO₂e data extraction from unstructured documents (70% reduction)

• Agentic AI pipeline for automated Scope 3 emissions calculation with 150,000+ validated data records

• Smart API workflows for real-time carbon footprint calculations in ERP and ESG systems

• ML algorithms to predict emission hotspots and optimize product design

• Automated data validation pipelines with NLP for quality assurance of CO₂e datasets

• Led a 15-person cross-functional team to develop 10+ AI features

• Strategic product planning and AI roadmap with 35% shorter time to market

• Stakeholder management with DAX companies (40% higher satisfaction, 95% retention)

• On-time project delivery with 95% budget adherence through data-driven backlog management

• Agile methods (Scrum, Kanban) with continuous AI/ML integration (25% team velocity increase)

• Product-market fit for AI features through A/B testing and analytics (60% higher adoption rate)

• Development of scalable backend applications with C#/.NET and Java
• Design and implementation of distributed microservice architectures
• Development and integration of REST APIs for industrial applications
• Development of modern web applications with React, Angular, and TypeScript
• Implementation of MQTT-based communication solutions
• Integration of industrial protocols such as OPC UA and Modbus TCP
• Development of batch, process control, and HMI components
• Containerization and deployment of applications with Docker
• Conducting code reviews and supporting architecture decisions
• Close collaboration with product owners, QA, and interdisciplinary teams
• Analysis of business requirements and implementation of technical solutions
• Further development of existing software architectures with a focus on maintainability and performance

Technologies: C#, .NET, ASP.NET Core, Java, C++, React, Angular, TypeScript, Vue.JS, MQTT, OPC UA, Modbus TCP, Docker, GitLab, MariaDB, MySQL, Linux

• Ride-sharing and bus platform, live and working. Backend Spring Boot 4.1 on Java 21, PostgreSQL with Flyway, Testcontainers integration tests. Running in my own AWS account (ECS Fargate, ALB, ECR, IAM least privilege) with CI/CD via GitHub Actions and OIDC federation without static credentials. Development throughout AI-assisted with Claude Code, including my own skills and project-specific memory. Spring Boot · Java 21 · PostgreSQL · Flyway · Docker · AWS ECS/ALB/ECR · CI/CD · GitHub Actions · Claude Code

• Definition and setup of the subject areas
• Building a governance model for the department with the areas of responsibility
• IT strategy, project management, process management, and quality and sustainability management
• Developing a communication strategy within the group
• Creating the IT strategy
• Designing templates, guidelines, and processes for consistent work
• Capturing strategic guardrails and grouping ongoing projects – deriving a roadmap for strategic planning
• Reviewing ongoing projects
• Creating staffing calculations and volume structure
• Defining job profiles

• 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.

• Designed and developed a multi-tenant SaaS platform enabling organizations to build their own knowledge bases and chat with brand-customized AI assistants (white-label approach with dynamic branding per organization).
• Implemented a scalable RAG architecture with a GPT-4o tool-use loop, hybrid semantic search, and strict tenant isolation at database and search index level.
• Built persistent, project-like chat sessions including a streaming API (SSE), multilingual support, and speech input/output (STT/TTS).
• Delivered the cloud infrastructure as Infrastructure-as-Code, fully automated per-customer CI/CD pipelines, and an onboarding process for new tenants.

Technologies used: Python, FastAPI, Pydantic (v2 noted), Next.js, React, TypeScript, Tailwind CSS, OpenAI / LLMs (GPT-4o), Azure AI Search, Cosmos DB, Azure Blob Storage, Azure Cognitive Services Speech, Azure App Service, Azure Container Registry, Retrieval-Augmented Generation (RAG), Server-Sent Events (SSE), Docker, Terraform, GitHub Actions, REST, OpenID Connect (OIDC), Multi-Tenancy

Driving AI model quality at scale — evaluating prompt-response accuracy, flagging edge cases, and maintaining SLA-compliant workflows across distributed global teams.

• Analyse AI prompts and side-by-side model outputs to assess response quality, factual accuracy, relevance, consistency, and compliance with project evaluation guidelines.
• Perform fact-checking, data validation, troubleshooting, issue identification, and edge-case review to improve quality standards across AI training support workflows.
• Use Google Sheets, Google Docs, and browser-based tools to document findings, maintain evaluation logs, track issue patterns, and support workflow optimisation in a remote environment.
• Create clear written justifications, review summaries, and KPI-oriented reporting focused on accuracy, turnaround time, documentation completeness, defect identification rate, and SLA adherence.

• Training the AI models, evaluating images and texts for UI/UX, turning the provided data into insights via OpenAI Feather as part of the machine learning workflow

Technologies: OpenAI Feather

• 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.

Enhancement and modernization of a mature Aspire backend application in the environment of a utility company, focusing on new business requirements, testing, legacy code cleanup, and stable backend delivery.

Core contributions & results Implemented new business requirements in the context of customer orders, subcontractors, and cross-company backend processes, and ensured consistent workflows in a distributed system landscape. Modernized existing backend components step by step and reduced technical debt through targeted legacy code cleanup, refactoring, and structured code reviews. Improved the testability of business-critical services by expanding automated tests with xUnit, AutoFixture, and clearer validation structures. Supported the further development of workflow automations and integration processes via microservices, messaging, and API-based communication. Took over source code from external firms, systematically checked code quality, and derived technical improvements for maintainability, stability, and integration. Worked in agile development processes with Jira, Confluence, and Azure DevOps and supported cross-team alignment on architecture, quality, and implementation. Technical metrics Technologies & methods C#, .NET, ASP.NET, ASP.NET Core, Aspire, Docker, RabbitMQ, gRPC, REST API, Swagger, Microservices, NServiceBus, AutoMapper, Autofac, xUnit, AutoFixture, FluentValidation, Entity Framework Core, MediatR, Redis, Consul, Serilog, SonarQube, Azure DevOps, Azure Monitor, GitLab, Google Protocol Buffers, IronPDF, Mailjet, Jira, Confluence, Miro, agile development, Scrum, code reviews, refactoring, legacy code cleanup, workflow automation, power grids