Freelancer profile translated to English.
Back to original languageAbout Maurice
Enterprise AI Architect with 10+ years of experience in product leadership, software architecture, and AI systems. I design scalable AI and platform architectures for complex ERP, CRM, and data landscapes, guiding companies from initial use cases to production-ready AI adoption.
Enterprise AI & Solution Architecture
Design scalable AI and Generative AI systems, define target architectures, distributed systems, API-first design, secure/maintainable enterprise platforms
GenAI & Intelligent Systems Engineering
Multi-agent architectures, RAG pipelines, prompt optimization, model fine-tuning, evaluation frameworks, production model deployment
AI Governance & Responsible AI
Regulatory alignment, model risk management, secure LLM integration, policy guardrails, audit logging, monitoring, and lifecycle governance for generative AI platforms
Multi-Cloud
Automated model training and deployment workflows, CI/CD pipelines, Dev/MLOps practices, monitoring, cost control, and performance optimization in GCP/Azure/AWS environments:
- AWS (Bedrock, SageMaker) - LLM orchestration, RAG architectures, model gateway patterns
- Azure (Azure OpenAI, AI Studio, Foundry AI) - enterprise LLM platforms & Copilot integration
- GCP (Vertex AI) - model training & deployment
Data & System Integration
Enterprise data pipelines, event-driven architectures, ERP/CRM integration, business system connectivity, scalable backend and API development
Delivery & Technical Leadership
End-to-end solution ownership (Discovery → MVP → Production), cross-functional collaboration, stakeholder alignment, structured rollout, and measurable business success
Vendor/Tool Evaluation & Build vs. Buy Strategy
Evaluation/selection of AI platforms and developer tools, including criteria systems, vendor comparison, security/compliance assessment, cost analysis, and build vs. buy decision support.
German
Native or bilingual
English
Fluent
Can work on-site
Hamburg (up to 50km), Frankfurt am Main (up to 50km), Berlin (up to 50km), München (up to 50km), Köln (up to 50km)
Experience
- Startup aus dem FinanzdienstleistungssektorAI & Integration Architect | Solution LeadSOFTWARE PUBLISHINGOctober 2025 - December 2025 (2 months)Hamburg, GermanyIntelligent Payment & Feedback Integration with AWS, HubSpot, and LLMsChallenge:Payment events and customer feedback were previously processed in a fragmented manner: different payment providers, lack of real-time synchronization with HubSpot, manual evaluation of free-text feedback, and no systematic prioritization of negative customer feedback. This led to delayed response times from service and account teams, incomplete CRM context, and unnecessary manual effort.Approach:
- Architecture design and implementation of a scalable AWS-based event integration platform for payment events.
- Automated enrichment of events via HubSpot API (contact, company, deal) to establish complete CRM context.
- Semantic analysis of free-text feedback using Large Language Models (OpenAI): detection of sentiment and topic category (e.g., Billing, Usability, Product). Creation of a concise summary including recommended actions.
- Automatic writing back of results to HubSpot (notes, custom objects).
- Proactive triggering of service tickets or tasks for negative feedback to improve response speed.
Value:- Real-time linking of payment, feedback, and CRM data without manual intermediate steps.
- Significant acceleration of service response times through automatic identification of critical customer feedback.
- Improved customer health scores and higher customer satisfaction through proactive team actions.
- Scalable architecture that can be used as a universal semantic feedback layer for any event, not just payments.
- High compliance and operational security through standardized cloud and governance mechanisms.
- Rivington TechAI Strategy & Enterprise Transformation LeadCONSULTING AND AUDITSJanuary 2024 - Today (2 years and 5 months)Responsible for the conception, management, and implementation of business-critical AI and automation initiatives as an external lead in enterprise mandates.Mandates & Responsibilities
- Leadership of AI strategy and transformation programs across Sales, Operations, and IT
- Design and implementation of AI and GenAI use cases in CRM, ERP, and operational system landscapes
- Sparring partner for C-level stakeholders on prioritization, governance, and scaling
- Leadership of cross-functional delivery teams (Business, Data, Engineering)
Selected Results- Reduction of manual effort in operational processes by up to 65-70% through AI-powered automation
- Implementation of AI-based CRM and feedback intelligence solutions with significantly reduced response times in customer service
- Establishment of scalable AI operating models in regulated environments
- Führender Finanzdienstleister in DeutschlandAI Strategy & Transformation LeadBANKING AND INSURANCEAugust 2025 - September 2025 (1 month)Köln, GermanyAI Use Case Discovery for Backoffice Process AutomationChallenge:The client's backoffice processes, including document processing, customer data verification, and administrative workflows, were heavily manual. Teams faced repetitive tasks, fragmented systems, and long processing times, limiting scalability and efficiency. Management sought to explore how autonomous AI systems could coordinate, execute, and optimize internal processes for higher operational efficiency and accuracy.Approach:
- Conducted an AI potential analysis across key backoffice workflows to identify automation and decision support opportunities.
- Analyzed process bottlenecks and data dependencies between CRM, document management, and compliance systems.
- Conceptualized and prioritized agentic AI use cases, including automated document classification, information extraction, and cross-departmental workflow orchestration.
- Developed a proof-of-concept architecture for integrating multi-agent systems into the existing IT landscape, ensuring compliance with data privacy and regulatory requirements.
- Created an AI adoption roadmap and a value model to quantify expected ROI and implementation effort.
Value:- Identified four high-impact AI use cases capable of reducing manual effort by up to 65%.
- Provided a blueprint for agentic process automation as a foundation for pilot projects in document processing and data validation.
- Empowered management to prioritize AI investments based on measurable business value and compliance readiness.
Agentic AI | Process Automation | Back-Office Optimization | Financial Services | AI Strategy | Process Orchestration | Data Compliance | Digital Transformation
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Education
- Forbes 30 Under 302019Forbes 30 Under 30
- Certified Scrum Product Owner2024Certified Scrum Product Owner
Certifications
- Microsoft AI Product ManagerMicrosoft/Coursera2025