Generative AI Experts in Germany

• Redesign of the company's BI infrastructure: replacement of a fragmented landscape of manually maintained Excel solutions and CSV imports with a centralized Power BI environment featuring a unified data model as the company-wide single source of truth
• Consolidation of previously isolated reporting logic into a central semantic model – eliminating redundant files, manual data transfers, and inconsistent metrics between departments
• Forecasting & planning: Design and implementation of company-wide liquidity planning in Power BI – from business logic to a fully automated, data-source-driven planning model replacing the previous manual Excel process; enables rolling forecasts and continuously up-to-date cash flow transparency for management
• Optimization of existing Power BI dashboards in terms of performance, structure, and analytical value using an AI-native approach
• Analysis and improvement of the data model, including data quality analyses, data cleansing, and consistent modeling using star schema, DAX, and Power Query
• Incident & anomaly analysis: Identification, investigation, and explanation of data anomalies, including root-cause analysis and concrete recommendations for action
• AI solution architecture: Connecting Business Central and Power BI to LangDock via MCP (Model Context Protocol) for AI-supported data usage
• Creation of a historical data layer as a basis for trend and time-series analyses
• AI-supported automation: Design and development of AI skills, agents, loops, and processes for the automated analysis and interpretation of reports
• Automated reporting workflow: Setup of scheduled, automated email distribution of AI-generated analyses and recommendations to stakeholders
• Gathering and documentation of business requirements and coordination with business departments and IT as part of requirements engineering / product owner activities
• Breaking down overall requirements into clearly defined work packages and tasks
• Definition, prioritization, and management of milestones throughout the entire project lifecycle

Tools: POWER BI, M365, Copilot Studio, MIRO, Microsoft Business Central, Microsoft Fabric, Claude AI, ChatGPT, LangDock, MS VS Code

• Generative AI products for corporate clients, owned from strategy through specification to production.
• Central strategy, local configuration: multi-tenant AI assistant for occupational pension schemes (bAV), delivered as an interactive avatar with text and voice path. Three tenants run on one codebase, each with its own conversation guide, while the knowledge base, guardrails and escalation paths stay central
• Versioned, AI-ready knowledge base composed into a tenant-agnostic voice context and tenant-specific text prompts — the configuration layer that keeps local adaptation from forking the product
• Conversational design: answer limits, scope and off-topic handling, anti-hallucination rules, escalation and lead handover to human advisors
• Five eval suites as a quality gate before any prompt or model change (anti-hallucination, LLM-as-judge failure modes, multi-turn consistency, voice KPIs, action vocabulary with confusion matrix); user test with 10 testers (Hamburg, 07/2026) drove the rework from alpha to beta
• Coordinated external developers, compliance and client stakeholders; GDPR-compliant EU stack, IDD-compliant, EU AI Act classification documented
• Second product line: white-label social media generator for consultancy chilli mind (CH/DE) — one codebase, per-client branding and configuration
• Results: 239+ deployments and a pilot with corporate customers · 108+ deployments for the white-label product · repeatable pattern for multi-tenant AI products in a regulated environment

Programming custom indicators with ProBuilder (for the ProRealTime platform)

6 months at RCP - Rabe Academy (basics - trading account - TWS Interactive Brokers) Focus: ETH price history - NVIDIA stock (NASDAQ and CBOE)

• 500 hours total (TradingView & ProRealTime), including approx. 1,500 hours of tick data analysis using ProRealTime

Systematic market analysis, development of custom indicators, pattern recognition.

• Support for a data, analytics and AI initiative in a regulated enterprise environment by structuring, evaluating and prioritizing several data-driven use cases based on business impact, feasibility, scalability, data maturity and governance requirements.
• Translation of complex business and analytics requirements into clear product, data and implementation logic, as well as preparation of decision-ready documents, target visions and roadmap inputs for stakeholder and management discussions.

• Hands-on development of AI-native product prototypes with specialized AI agents for research, requirements, business logic, UX/flow design, test case generation and quality assurance.
• Structured use and orchestration of AI agents through clearly defined roles, inputs/outputs and handover points; breaking down complex product tasks into verifiable work packages and iterative prototyping cycles.
• Establishment of human-in-the-loop quality gates to validate AI-generated results for functional correctness, consistency, completeness and feasibility; targeted rework cycles in case of deviations.
• Development of a regulatory GenAI/rules prototype for CRD VI with a structured decision flow, web UI, rule-based validation and automated test cases; iteration of the business logic through to a pilot-ready POC.
• Design of an AI-to-Action banking prototype: AI intent → consent → bank/product logic → conversion including admin console; translating the product idea into MVP scope, role model, user flows and clickable prototypes.

CXO AI OS is an agent system made up of six building blocks. Instead of using AI as a chat window, it creates a system that understands a company’s context, makes decisions according to its rules, and acts on its behalf.

• For mid-sized companies: a guided sprint followed by operation for a team, department, or prioritized cluster, based on an AI assessment
• For self-employed professionals: a program in which participants build their own agent system
• Sequence in the company: assessment, prioritization, sprint, operation
• Implementation in Claude Cowork or ChatGPT Work, without coding
• Architecture: Chief of Staff, Goals, Advisors, Agents, Context, Catalog

Project: Defect Generation on Test-Bench Images of Metal Surfaces Environment: Automated Visual Inspection (AVI), Metallurgy & Manufacturing

• Objective & Implementation: Designed, architected, and trained 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 test-bench lighting conditions for privacy-compliant and efficient dataset expansion (data augmentation).
• Technical Design: Implemented robust Generative AI and computer vision pipelines in Python and PyTorch. Used semantic segmentation approaches for mask-controlled defect synthesis and subsequent evaluation with EfficientDet object detection models.
• Business Impact: Massive dataset upscaling (10x) without time-consuming and costly physical test-bench runs, while significantly improving the detection performance of automated inspection systems.

Technologies & Skills Used: Python | PyTorch | SPADE | Pix2PixHD | EfficientDet | Machine Learning | Semantic Segmentation | Computer Vision

• Development of an agent-based governance platform for the automated assurance of EU AI Act compliance and ODA-compliant orchestration of AI services in complex enterprise environments.

• Design and implementation of an agent-based "Mission Control" framework (Aletheia Conductor) for autonomous state monitoring and process control.

• Development of "Compliance-as-Code" (CaC) solutions based on OPA/Rego for system-wide enforcement of regulatory guardrails.

• Integration of TM Forum ODA standards (TMF630, TMF622, TMF642) to ensure interoperability and standardization.

• Building a highly available event-driven architecture using Redpanda and CloudEvents v1.0 for near-real-time event processing.

• Implementation of an audit-proof "Evidence Chain" through cryptographic linking of trace logs in preparation for automated audits.

• Tech Stack: Java 21 (Quarkus Native), TypeScript (Next.js), Redpanda (Kafka API), CloudEvents v1.0, OPA (Open Policy Agent) & Rego, TimescaleDB, ZincSearch, Redis, TM Forum ODA, Git, GitHub, Clean Code Development, Like-C4.

• Developed a browser-based platform for Kubernetes no-code deployment and cluster management
• Developed a CLI in TypeScript to deploy resources in the cluster without leaving the browser UI.
• Implemented DevSecOps pipelines: image scanning, SBOM, policy enforcement, supply-chain security, and used Kyverno. Implemented IAM integration for the command-line utility tool.
• Designed role and permission models for Keycloak, OAuth/OIDC, and social login flows.
• Used LLMs to convert user intent into diagrams.
• Worked on integration with multiple sovereign clouds like StackIT, Hetzner, CIVO, UpCloud, plus public clouds like AWS, GCP, and Azure
• The technology stack includes Java, Spring Boot, Kubernetes, OpenAI, Kubernetes multi-tenancy using vCluster, Karpenter, RBAC for CLI, Helm, React

Stabilization of 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

• Lecturer in an AZAV-certified, one-month training program on AI basics and practical application
• Design and delivery of modules for participants from different professional fields
• Teaching how generative AI works, where it can be used, and where its limits are, as well as prompting and evaluation practice
• Creating training materials and exercise formats
• Trainer qualification according to AEVO

Remote freelance role focused on Agentic AI strategy, secure application patterns, and reusable agentic workflows for a government agency.

• Development and implementation of an open source Agentic AI strategy for a government agency, with a focus on GDPR, security, and self hosted solutions
• Development of reusable agentic workflows and mini applications that enable non technical employees to solve business problems independently
• Implementation of internal business applications with Single Sign On (SSO) and Azure PostgreSQL integration on Hetzner Linux servers
• Implementation of nine mini applications with Single Sign On (SSO) and Azure PostgreSQL integration on Hetzner Linux servers
• Techstack: Python, Nextjs, Typescript, Streamlit, Anthropic SDK (Claude), Azure, Linux Ubuntu, PostgreSQL, MS SQL, Angular, Authentik

Time-limited interim assignment filling a vacant program leadership position: joint leadership of a complex software project and orchestration of several agile teams (business, development, and DevOps teams), as well as actively driving change processes.

• Program Management & Planning: Responsibility for creating and updating milestone, resource, and budget plans to ensure project objectives using agile and hybrid methods.
• Cross-functional Collaboration: Promoting cooperation and knowledge sharing across teams; resolving blockers through proactive conflict management and targeted facilitation.
• Stakeholder Management & Transparency: Ensuring transparent and audience-appropriate communication within and outside the project organization using modern project management tools.

• Develop custom AI agents delivering 90%+ reduction in manual workload and significant efficiency gains
• Provide end-to-end AI strategy consulting: from digital assessment to implementation and change management
• Design and deliver tailored training programs and workshops on AI adoption, prompt engineering, and automation
• Support clients in implementing scalable AI solutions integrated with existing technology stacks
• Focus areas: AI strategy, automation, workflow optimization, and capability building