Cloud Engineers in Germany

• Create and lead roadmap forecasting of support opportunities, including colleague training, process design and improvement, and new hire onboarding.
• Collaborate seamlessly with cross-functional teams including Professional Services, Support, Product, Sales, and Solution Engineering.
• Manage business and financial KPIs in alignment with overall business objectives.
• Industrial IT & Data Integration: Implement, configure, and integrate OT/IT systems and data flows, connecting control-layer sources (DCS, PLC, SCADA) to historians, data platforms, and enterprise systems through APIs and standard interfaces.
• AVEVA PI & Historian: Configure, maintain, and extend AVEVA PI (PI Data Archive, Asset Framework, interfaces/connectors) to capture, contextualize, and serve real-time and historical operational data.
• Alarm Management: Support and optimize alarm management systems – e.g. Honeywell Alarm Management – including alarm rationalization, classification, and the analysis of alarm performance, critical alarms, and alarm floods.
• Connectivity & Protocols: Set up and troubleshoot industrial connectivity using OPC UA and OPC A&E, including the configuration of tunnelers / wrappers to bridge OT data sources securely and reliably.
• Production Data Flow: Ensure seamless, secure, and well-structured data flow across the industrial IT landscape, transforming raw operational data into meaningful information for operations and decision-making.
• System Support & Troubleshooting: Provide ongoing support for OT/IT applications, troubleshooting issues across networks, software, and data integrations to keep systems running efficiently.
• Knowledge Management & Best Practices: Contribute to internal documentation, best practices, and troubleshooting guides, and help continuously improve how we deliver industrial IT and data solutions.

Tools & Software: Aveva PI, Salesforce, Active Directory, Microsoft Dynamics 365, Jira, Microsoft, Scrum, DevOps, Docker, CI/CD, Jenkins, GitHub.

• 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

• Responsible for the backlog and roadmap of a production SaaS compliance platform for the EU Deforestation Regulation (EUDR), with around 30 customers on AWS and Docker.
• Coordinated feature development and API integrations across 2 external and internal development teams.
• API integrations with the EU EUDR API and SAP.
• 70% fewer customer-reported issues, supporting ISO-27001 certification.

• 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

Financial services provider, >10,000 employees

• Entra ID: App Registrations, Hybrid Identities, group and permission concepts, Conditional Access, MFA, RBAC, and PIM.
• Azure IaaS/PaaS, Networking, Security, Governance, Monitoring, and automation with Bicep and PowerShell.
• Consulting and technical support for internal cloud projects.

Red Hat OpenShift Container Platform

Scope:

• Operation and configuration of the Red Hat OpenShift Container Platform, including network, storage, and security design.
• Implementation of centralized observability solutions (Prometheus, Grafana, alerting).
• Development of CI/CD pipelines, GitOps workflows, and automation frameworks.
• Training and enablement of operations and development teams.

Result: Accelerated service delivery, transparent operational metrics, and sustainable knowledge transfer.

To achieve savings and optimize compliance, apps were restructured in line with the mappings in identity management, restrictions were defined and, above all, costs resulting from overuse were reduced. Communication with stakeholders, validation of authorizations with users and technical implementation in the SAP FI/CO and Sourcing & Procurement modules created significant added value for the group. This also included the corresponding documentation for the auditors.

Cloud Journey Kickoff on STACKIT

• Helped the client kickstart their cloud journey on sovereign EU STACKIT platform.
• Bootstrapped IAC & GitOps architecture with Opentofu and ArgoCD.
• Setup platform fundamentals for a fresh, holistic cloud experience: Kubernetes, external secrets, certificate management, gateway API, DNS & routing, observability basics.

• Modernization of a large-scale Java enterprise application*

The project is modernizing a complex enterprise application that has grown over many years. The existing Spring-based legacy system runs on Java 8, OSGi, and Eclipse RCP and is being gradually migrated to a modern, maintainable architecture with Java 25 and Quarkus.

Marcus works on analysis, architecture, refactoring, and implementation. One focus is on untangling historically grown structures and dependencies and on building a clean, sustainable Java and Quarkus technology stack.

Tools & technologies: Java 8, Java 25, Quarkus, Hibernate ORM with Panache, EclipseLink, OSGi, Eclipse RCP, Maven, JUnit, Mockito, REST, JSON, Git, Eclipse IDE, IntelliJ IDEA Ultimate, Jira, Confluence

• 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

Project: Design and implementation of an AI product for four business use cases

Project management for an AI project at a leading provider of large-scale catering and food services, where a production-ready AI product for four use cases was implemented together with an external development partner: automated briefings based on CRM and document data, voice-based capture and structuring of reports, detection and consolidation of duplicates in master data, and data-based market analyses. A key focus was a data-protection-compliant architecture that passed the internal IT security review and enabled production use.

• Translation of business requirements into clearly defined AI use cases with a focused product scope and clear value proposition
• Selection and evaluation of models and architecture options for text extraction, speech-to-text, and context enrichment from business systems, including LLM integration, function calling, and retrieval
• Development and implementation of an architecture with European hosting, data minimization, and masking of personal data as a prerequisite for approval
• Management of interfaces between business departments, IT, IT security, and the external development partner under restrictive data access conditions
• Coordination with the CIO and executive management levels on data access, risk assessment, and approval decisions
• Preparation for the transition to production use

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.

Conversational AI assistant for cloud infrastructure and security queries

• Designed FastAPI backend with multi-turn conversation handler, token budgeting, and context window management.
• Integrated Amazon Bedrock (Claude 3 Sonnet/Haiku); built RAG pipeline with MongoDB chat history and semantic search.
• Implemented Factory Pattern for modular LLM provider switching; reduced model onboarding effort by 60%.
• Reduced LLM inference cost by 35% through model tiering (Haiku vs Sonnet) and prompt/entity consolidation.

Tech: Python, FastAPI, Amazon Bedrock, MongoDB, Streamlit, Pydantic.

• Onboarded and conducted comprehensive documentation and system analysis to assess the existing data infrastructure, facilitating rapid integration and collaboration across functional data teams (modelling, processing, reporting).
• Collaboratively defined the architecture and project structure for a central data pipeline repository, including hierarchical standards, knowledge management strategies, and role-specific responsibilities, enhancing maintainability and onboarding speed.
• Evaluated and validated open-source data routing tools (Airbyte, Apache NiFi, Dragster) for ingest and sync requirements in retail analytics, including local benchmarking and error-state testing.
• Led the design and deployment of Airbyte in Kubernetes, creating customized Helm charts, securing secrets handling, and configuring Ingress with TLS and internal DNS routing, ensuring full API and UI accessibility.
• Troubleshot and resolved Ingress controller issues, iterating through multiple stages of debugging and testing, and documented setup and replication steps for scalable reuse.
• Mapped data models to ARTS standard, supporting schema alignment for ERP and reporting use cases, and coordinated review loops to align future data processing logic.
• Drafted strategic 1-pagers comparing MinIO, Pub/Sub, and routing architectures, providing technical guidance for architectural decisions and investment planning.
• Enabled secure access and authentication mechanisms, including initial evaluation for SAML integration, cluster-level configuration reviews, and service annotation improvements.