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AI / Agentic Solution Architect – Customer Self-Service (m/f/d) (DE)
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We are currently looking for a freelance "AI / Agentic Solution Architect – Customer Self-Service (m/f/d)" for our client in the IT sector. Start: asap End: 31.12.2026 Capacity: Fulltime Location: Remote Define and drive the AI and agentic architecture for an enterprise Customer Self-Service platform, enabling customers to interact with products, infrastructure, software, services and commercial offerings through intelligent conversational and task-oriented experiences. The architect will design how AI agents, MCPs, enterprise APIs and workflows work together to help customers perform self-service activities across their lifecycle. Key Responsibilities: ·Define the AI and agentic architecture for the Customer Self-Service platform. ·Design conversational and task-oriented agents capable of executing end-to-end customer journeys. ·Define MCP/tool/API patterns allowing agents to securely interact with enterprise systems. ·Design agent orchestration, context management, memory, human-in-the-loop and multi-agent patterns where appropriate. ·Enable self-service scenarios such as: ocustomer onboarding and information collection, ocreating and managing change requests, omanaging products and solution lifecycle, ofund and commercial consumption management, ousage and consumption analysis, oinfrastructure/software/service requests, oguided troubleshooting and customer support. ·Integrate AI capabilities with SAP S/4, SAP BTP and other enterprise platforms. ·Define authentication, authorization and customer-specific data boundaries for agent actions. ·Establish AI governance, guardrails, observability, evaluation and auditability. ·Define reusable AI platform capabilities rather than building isolated agents for every use case. ·Drive architecture from prototypes through production adoption. Key Skills: ·Agentic AI / LLM solution architecture ·MCP, tool calling and enterprise API integration ·Agent orchestration and workflow design ·Conversational AI and customer experience ·SAP BTP and enterprise integration ·REST / OData / event-driven architectures ·Identity, authorization and security ·AI governance, guardrails and observability ·RAG, context engineering and enterprise knowledge integration ·Cloud-native architecture Preferred Experience: Experience with technologies such as OpenAI, SAP Generative AI Hub / AI Core, MCP, LangGraph/LangChain, Semantic Kernel or comparable agent platforms. Experience designing enterprise customer portals, digital self-service platforms, subscription/consumption businesses, or B2B customer journeys would be highly valuable. What We Are Looking For: An architect who can move beyond traditional chatbot use cases and design an agentic self-service platform where AI can understand customer intent, retrieve the right enterprise context, guide the customer, invoke authorized business capabilities and safely complete tasks on the customer's behalf. The architect should combine: AI Architecture + Enterprise Integration + Customer Self-Service + Agentic Experience Design.
- Define the AI and agentic architecture for the Customer Self-Service platform.
- Design conversational and task-oriented agents capable of executing end-to-end customer journeys.
- Define MCP/tool/API patterns allowing agents to securely interact with enterprise systems.
- Design agent orchestration, context management, memory, human-in-the-loop and multi-agent patterns where appropriate.
- Enable self-service scenarios such as:
- customer onboarding and information collection,
- creating and managing change requests,
- managing products and solution lifecycle,
- fund and commercial consumption management,
- usage and consumption analysis,
- infrastructure/software/service requests,
- guided troubleshooting and customer support.
- Integrate AI capabilities with SAP S/4, SAP BTP and other enterprise platforms.
- Define authentication, authorization and customer-specific data boundaries for agent actions.
- Establish AI governance, guardrails, observability, evaluation and auditability.
- Define reusable AI platform capabilities rather than building isolated agents for every use case.
- Drive architecture from prototypes through production adoption.
- Agentic AI / LLM solution architecture
- MCP, tool calling and enterprise API integration
- Agent orchestration and workflow design
- Conversational AI and customer experience
- SAP BTP and enterprise integration
- REST / OData / event-driven architectures
- Identity, authorization and security
- AI governance, guardrails and observability
- RAG, context engineering and enterprise knowledge integration
- Cloud-native architecture
Francesca Hameister