Head of AI in Germany

• Governed multi-agent orchestration for regulated, EU-based companies – self-hostable, compliant with the EU AI Act and GDPR („by design“), BYOM (own models/GPU).
• Two-gate governance: agent deliberation + mandatory human approval, full signed audit trail; graduated autonomy model („internal → autonomous per skill“).
• Verified knowledge graph („Company Brain“) with source evidence for every answer; own orchestration framework (Virtual Team Framework).
• Industry solutions for financial services: compliance monitoring, invoice and contract review; hands-on development with LLMs (including Anthropic/Claude), agentic workflows, RAG.
• Building the governance-focused multi-agent platform TeamIntel (see AI reference projects).

• Spearheaded AI-driven projects to enhance cognitive development, showing a strong commitment to innovative solutions in the AI space.
• Led AI strategy and implementation across multiple projects, ensuring alignment with business goals and driving major progress in AI applications.
• Oversaw the development and rollout of cutting-edge AI technologies, fostering a culture of continuous improvement and innovation within the team.

• Lead ARRCH® & AI Design Sprint® implementations (1:1, team, org-level)
• Drive ROI-in-Weeks pilots: average pay-back in 8 weeks, 25 %+ cost-to-value delta
• Coach decision-makers to own an AI narrative that sticks with boards & teams
• Build repeatable systems, no one-off hacks, so capabilities scale, not just demos
• Trained 300+ professionals, achieving 72 % daily ARRCH adoption after 30 days
• Validated a median of 4 use-cases per sprint, with 2 entering production within one quarter
• Rolled out Microsoft 365 Copilot with 10 % month-on-month adoption across 5 departments

• Serve as AI lead for life-science solutions, integrating advanced AI models directly into company workflows and ensuring seamless deployment.
• Design and implement deep learning architectures (PyTorch, Keras) for complex biomedical challenges, including cell segmentation, multimodal omics analysis, and prediction of point clouds.
• Develop and deploy robust LLM-based systems, including RAG architectures and agentic workflows using LangGraph, to facilitate natural-language interaction with complex medical data.
• Lead cross-functional initiatives to apply foundation models and explainable AI (xAI) to clinical and evolutionary algorithms.

• Leading 20+ staff in development, site reliability engineering, and architecture.
• Leading the group-wide agentic AI initiative (Norway, Poland, Germany).
• Hands-on solution architect and AI consultant for over 50% of my working time on client projects in the publishing sector – from local publishers to international corporations.
• Strategic consulting and technical implementation of AI workflow platforms (n8n, Workato).
• Developing prototypes for traditional, AI-based, and agentic AI workflows.

• C-Level AI strategy consulting for companies in software development, transport and logistics, industry, medical technology, automotive, e-commerce, and event marketing.
• Leading the launch of new AI products and services.
• Analyzing use cases in R&D, sales, marketing, and service.
• Introducing AI solutions into everyday work.
• Technologies: Microsoft Copilot, Microsoft Azure OpenAI, AWS S3, Jira, Confluence.

Geeks Ltd is a UK-based technology company; WordUp is its AI-driven language-learning product focused on personalized vocabulary learning and intelligent educational experiences.

• Coordinate AI product delivery across Product, Engineering, Data, Operations, and leadership, translating user needs into scoped initiatives, sequencing work, surfacing blockers, facilitating hand-offs, and communicating progress.
• Own search, recommendation, retrieval, and content-enrichment features end to end, from requirements and architecture through Python/FastAPI implementation, testing, deployment, monitoring, and rapid iteration.
• Developed low-latency retrieval, ranking, and personalization services using AWS, OpenSearch, DynamoDB, embeddings, and reusable APIs, achieving <1s latency, 22% higher engagement, and 12% higher premium conversion.
• Use AI coding assistants for codebase analysis, scaffolding, refactoring, tests, debugging, and documentation while reviewing every output for correctness, architectural fit, security, maintainability, and user value.
• Represent technical work in planning and stakeholder discussions, gather requirements first-hand, challenge priorities constructively, explain delivery trade-offs, and help teammates make outcome-focused decisions.

• Analyzed patient data for various clients
• Developed complex analysis pipelines
• Performed quality assurance on methods

• Technologies: GoLang, JavaScript, TypeScript, AWS, Terraform, Git
• Conception of AWS infrastructure and existing services
• Analysis of IAM accounts and roles
• Setup of Cost Explorer and CloudWatch monitoring
• Setup of DynamoDB and S3 persistence of collected information
• Reporting and cost calculation
• Conception of Terraform deployment