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Azure AI Foundry, GitHub Copilot (Agent Mode), Model Context Protocol (MCP), Kubernetes, CloudFoundry, Terraform, GitHub Actions, Langfuse, Python, Grafana

Objective: Accelerate enterprise-wide developer enablement and migration from GitLab/Jenkins to GitHub through secure CI/CD standards and automated, agentic developer support.

• Architected & deployed an enterprise-grade AI Support Agent integrated into GitHub Copilot via MCP, enabling developers to query legacy Confluence docs and Git repositories contextually.
• Designed & standardized secure, reusable GitHub Actions "Golden Path" templates, accelerating onboarding and ensuring compliance-by-design for delivery teams.
• Built and engineered robust data pipelines to establish a DevOps Maturity Model, monitoring platform adoption and migration KPIs via Grafana and Azure Monitor.
• Established LLM observability and evaluation frameworks utilizing Langfuse and Azure Monitor to optimize agent responses and control token costs.

Scope: Embedded as FP&A Data & AI Architect within the Finance function of a major European refrigeration manufacturer, leading the transformation of manual, fragmented financial reporting into an automated, governance-driven intelligence platform. Driving the shift from Excel-based controlling to structured data architecture, Power BI analytics, and AI-assisted financial operations.

Financial Data Integrity & ERP Governance

• Initiated and led GL vs. subledger reconciliation investigations, identifying and resolving structural mismatches between General Ledger and subledger data that had gone undetected prior to engagement
• Conducted asset analysis to identify items missing from General Ledger postings, surfacing gaps in fixed asset tracking and period-end completeness
• Validated SAP reports, establishing baseline data quality standards for Finance team consumption
• Established systematic SAP data validation framework ensuring ongoing integrity between ERP postings and downstream reporting outputs

Finance Reporting Transformation

• Designed and implemented a structured Transformation Project approach for converting manual Finance reports into fully automated processes
• Created and owns the Data Reporting Audit Log; a centralized tracking system capturing report owners, stakeholders, data sources, manual effort estimates, and automation opportunity scores across the Finance function
• Mapped the full reporting landscape identifying quick-win automation targets and strategic Power BI migration candidates
• Actively reducing manual Excel and PowerPoint dependency across FP&A workflows; replacing point-in-time snapshots with live, governed data models

Power BI & Analytics Enablement

• Introduced and presented Power BI as the strategic reporting platform to Finance leadership, building internal buy-in for the BI transformation roadmap
• Designed initial Power BI architecture aligned with SAP, Salesforce and Oracle data structures and FP&A reporting requirements
• Established report ownership, governance documentation, and data lineage standards enabling sustainable self-service analytics across the Finance team

Transformation Infrastructure & Collaboration

• Configured and deployed Jira as the transformation project management hub, establishing structured sprint workflows, backlog management, and progress visibility for Finance IT initiatives
• Proposed and initiated a dedicated FP&A Communication & Transformation Hub, a structured cross-functional forum aligning Finance, IT, and business stakeholders around the reporting transformation roadmap
• Positioned the Finance function as an active driver of data governance and digital transformation within the broader organization

Outcomes

• GL/subledger reconciliation gaps identified and investigation framework established within first two weeks of engagement
• Data Reporting Audit Log deployed; first structured inventory of Finance reporting landscape in company history
• Power BI transformation roadmap presented and approved by Finance leadership
• Jira-based project governance live; Finance transformation now tracked with full sprint visibility

Technologies: SAP FI/CO · Power BI · DAX · SQL · Excel (advanced) · Power Query (M) · Power Automate · VBA · Jira · Microsoft 365 · SharePoint · Salesforce (Sales Data) · Oracle HCM · Python

• Technical program ownership for the IPv6 migration at DRV RP and DRV BW, with a focus on migration planning, execution structure, and cross-functional technical coordination.
• Designed and implemented an operational control model with dashboard, action board, KPI portfolio, risk register, and decision index to translate technical topics into structured delivery artifacts.
• Coordinated technical groundwork for architecture and rollout across IPv6 addressing, segmentation, dual-stack target design, test-lab planning, and cross-team dependencies.
• Supported security and compliance-related requirements in the context of BSI, NIS2, and critical infrastructure, translating them into traceable evidence, risks, and management reporting.
• Achievement: Established a reusable intake-to-governance workflow for systematically capturing technical actions, risks, open issues, and evidence requirements.
• Achievement: Created an operational baseline for technical program execution with measurable KPIs, clear ownership, and transparent decision support.

• Held direct line management responsibility for a team of 4 Platform Engineers — owning hiring, performance reviews, and career development — while establishing a shared engineering standards framework and coaching culture that accelerated delivery across client engagements.
• Led a team of engineers to architect a cloud-native voice AI system for a major inspection client, enabling 2,500 field inspectors to document work fully hands-free via real-time transcription and AI agents — eliminating manual data entry across 440,000 inspections per month and reducing per-user cost from $9 to $1. Stack: AWS (DynamoDB, S3, Transcribe, CloudFront, API Gateway, Bedrock), ElevenLabs, Claude.
• Led a team of engineers to automate multi-region Kubernetes cluster management for a global SaaS leader, reducing provisioning time from 3 weeks to under a day and eliminating 90% of configuration errors. Stack: EKS, Terragrunt, Python, Bash, ArgoCD.
• Accelerator - Cloud-Agnostic AI Platform: Architected and delivered a cloud-agnostic, Kubernetes-native platform as an accelerator, enabling multi-tenant, enterprise-scale management of self-hosted LLMs with concurrent deployment of multiple base models and dynamic LoRA adapter serving. Designed production infrastructure using open-source tooling (ArgoCD, Karpenter, vLLM, SGLang) with automated model lifecycle management, API security (Keycloak + LiteLLM), and cost-optimized GPU provisioning.

AI-powered platform for automated UX validation and designer support

• Designed and led technical implementation of an enterprise-wide AI solution for automated UX review that improved design quality and significantly reduced manual review processes in teams
• Developed an automated UX validation tool as a Figma plugin and web application that generates test cases based on internal guidelines and reliably checks current designs for consistency and standard compliance
• Implemented an interactive designer chat based on RAG that answers questions about the current design and the company's UX guidelines, and designed the deployment architecture using containerized services
• Python, Azure OpenAI, PostgreSQL, REST API, Docker, OpenShift, Helm, CI/CD, Figma MCP, LLM, RAG, Prompt Engineering, GenAI, XAI, AI Architecture, AI Strategy

• Development of a SaaS application for automated claims handling with AI agents (LLM) as the primary development team
• Design & testing of efficient and secure context management setups in the development process (including multi-sub-agent use, memory systems, caching)
• Definition and implementation of LLMOps pipelines with Azure AI Foundry for AI agents in customer contact (including versioning, logging, audit trail, security tests)
• Infrastructure provisioning via IaC (Bicep), application configuration via GitOps-based CI/CD pipelines (rules engine, workflow engine)
• Development of integrated security architecture designs between AI-based & classic applications with a special focus on regulatory requirements
• Integration of workflow and rules engine in a NestJS service architecture — for automated, rule-based control of claims processes
• Probabilistic extraction and preparation of claims data as the basis for rule-based, deterministic decision logic — traceable, auditable, and regulatorily compliant

Evaluation, structuring, and handling of complex tenders for Sogeti as well as as part of larger Capgemini proposals. Focus on analyzing demanding customer requirements, deriving solid solution concepts, and translating technical, functional, and commercial aspects into convincing proposal storylines. In close alignment with delivery, sales, account, and bid teams, scalable and implementable Quality Engineering solutions were designed, evaluated, and prepared for customer decisions. This included selecting suitable role profiles and right-shore staffing options, assessing available skills and capacities, as well as the commercial preparation of reliable price and effort calculations.

A special focus was on bid management and handling service proposals (RfP/RfI analysis), solution design, presentation and management materials, as well as the functional and technical evaluation of customer requirements in the context of Quality Engineering & Testing, agentic AI-supported test automation using modern quality assurance approaches.

SOGETI projects:

• BMW: Development of a test strategy including test management, test automation, and provision of a rightshore delivery model.
• GEA: SAP Business Assurance and Quality Engineering for an SAP rollout including test governance, test automation, AI agent layer, and hypercare concept.
• AIXTRON: SAP S/4HANA Cloud migration explore phase with QA assessment, testable blueprint structure, staffing, and scalable Sogeti delivery approach.
• Deutsche Glasfaser: Quality Engineering & Testing for DG development squads, including ServiceNow and Salesforce, testing automation and governance.
• ERGO: RfP/proposal support for Managed Service Test Data Provisioning as well as a Jira/Atlassian rollout with assessment, target picture, MVP, rollout, and governance.
• Munich Re: Proposal and solution design in the insurance environment with a focus on test data provisioning, process analysis, compliance, and automation.
• BWI: Project/proposal context in the public/IT service environment with reference to Quality Engineering, Test Factory, and digital transformation support.
• Rolls-Royce: Proposal/project support in the Sogeti context with a focus on structured RfP analysis, solution design, and customer-oriented proposal preparation.

• Consultant for system architecture, AI agents & integration, coach for data & process logic, Graph-RAG approaches, security and data protection.
• On-premise AI solutions with high compliance and performance requirements.
• Architecture decisions, operational setup, strategic prioritization & deployment.
• Technologies: LiveKit JS SDK, LiveKit Agents, Web Audio API, JS, AudioWorklet, Loki, vLLM, Zscaler, Docker, Neo4j, MySQL, Python.
• Models: GPT-OSS 20B, Whisper large v3 turbo, Qwen3-TTS.

• Technical lead of a team developing agentic chatbot solutions (React, TypeScript, Python, FastAPI)
• Architecture design for multi-LLM dialog systems - focus on maintainability, UX, and autonomous execution
• Stakeholder alignment, CI/CD processes, and AI integration at enterprise level

As the Solution Architect for Agentic AI in auto claims processing, I led global customer delivery implementations, encompassing solution design and detailing, multi-tenancy, process flows, integration with third-party solutions, and localization requirements.

• Architectural Analysis: Conducted in-depth analysis of business requirements, managing requirements and creating detailed specifications.
• Service Definition: Developed comprehensive technical definitions for services and integration contracts.
• AI Process Management: Automated AI process management, focusing on analysis, optimization, and continuous improvement.
• Requirements Gathering: Facilitated requirement-gathering sessions and analyzed business processes to identify optimization opportunities.
• Agile Collaboration: Employed agile methodologies, working closely with stakeholders to ensure alignment and responsiveness.
• Technical Support: Assisted senior management with technical analyses and deliverability assessments.

• Integrated LangChain middleware between AEM and SAP PIM system
• Developed a FastAPI interface for system communication
• Implemented vector embeddings for semantic product search
• Evaluated LLM models (Vertex AI/Gemini, LM Studio, Hugging Face, OpenAI) for product analysis
• Developed an AEM component to display product recommendations and integrated the recommendation API into the AEM authoring process
• Designed and implemented Pinecone vector database for product embeddings
• Optimized response times and caching strategies
• Evaluated Vertex AI Studio for LLM testing and prompt workflows
• Implemented secure API routing and access control for AI components via FastAPI and gateway validation

• General CTO responsibilities (architectural design, operational setup, external runtime product evaluation, investor buy-in, regulatory compliance).
• Software development oversight (implementation on deep-dive-in) plus workflow design.
• Product co-ownership.
• Tech/tools/frameworks: proprietary software (Java, JavaScript), Kubernetes, Postgres, MiniIO, Ollama (internal), several xAI API (external), OpenTofu (Terraform), Keycloak, Kafka, Prometheus, ELK Stack, GitHub, GitHub Workflows, Argo CD, ISO 27001, BSI-ISM, EU AI Act.

• Clarification of project goals, scope, and functional target vision for building the AI-based knowledge base.
• Deriving the initial architecture and implementation strategy for the Zasterbot chatbot, including defining the MVP and expansion phases.
• Developing a functional target vision for building a structured knowledge base and integrating a future chatbot.
• Deriving and prioritizing use cases for information retrieval and provision by the chatbot.
• Modeling data structures and flows for effectively organizing the knowledge base on the Base44 platform.
• Designing and implementing data models for storing and linking relevant information.
• Developing processes for extracting, analyzing, and preparing raw data for the knowledge base.
• Ensuring data consistency and quality as the foundation for the future chatbot.
• Planning the integration of large language models (LLMs) and retrieval-augmented generation (RAG) for precise and context-aware responses.
• Implementing features for analyzing and visualizing data from the knowledge base.
• Using the Base44 platform with JSON-schema-based entities and a flexible permission model.
• Implementing Deno functions for backend logic, event processing, and external API integration.
• Integrating OpenAI services for initial data analysis.