Senior Technical Architect – Hybrid (Product / Platform) (m/f/d)
Your mission
Cloud Native Node.js & AI-enabled Product with SAP Private Cloud Integration | Senior / Principal LevelAs Senior Technical Architect - Hybrid, you own the technical design across our two worlds: the cloud-native, AI-enabled product line built on modern Node.js architectures, and its integration into the SAP Private Cloud platform landscape.
Your focus is approximately 70% Public Cloud product architecture and 30% SAP Private Cloud platform architecture. You are the architect who makes the hybrid seam work - ensuring that public cloud innovation and the SAP core evolve as one coherent, scalable, and future-proof landscape rather than two disconnected estates.
Your Responsibilities
- Cloud-Native Product Architecture (Primary Focus)
API-first and event-driven architecture concepts
Definition of service boundaries, data flows, and integration patterns
Ensuring multi-tenant capability and tenant isolation
Non-functional design: performance, scalability, resilience, cost
- AI Integration in Product
Integration of LLMs or AI services within defined guardrails
Design of RAG and agentic patterns over product and platform data
Performance and cost optimization of AI-driven features
- Hybrid Integration Architecture (Public ↔ Private Cloud)
API and event contracts across the hybrid boundary (OData, REST, SAP Business Events, Event Mesh)
Data ownership, consistency, and synchronisation patterns across both estates
Identity, authorisation, and trust propagation across the hybrid landscape
- SAP Private Cloud Platform Architecture (Secondary Focus)
Enforcement of clean core principles and SAP development standards
Architectural review of SAP-side designs together with the SAP Private Cloud & SCM architect
Awareness of core SCM process flows (SD, MM, EWM) as they surface into product capabilities
- DevOps & Deployment Architecture
Architecture design for containerization and cloud deployment
CI/CD and environment strategy spanning public and private cloud pipelines
- Design Governance
Technical review and sign-off responsibility in the Architecture Board
Translation of enterprise guardrails into actionable design standards for teams
Organizational Context
- Embedded in the Engineering function
- Senior / Principal Level
- Part of the Architecture Board
- Operates within guardrails defined by the Enterprise Architect
- Works in close tandem with the Technical Architect - Public Cloud and the Technical Architect - SAP Private Cloud & SCM
Your profile
- Several years of experience as an architect in cloud-native environments
- Strongexpertisein Node.js
- Experience with microservices and API architectures
- Strong understanding of event-driven systems
- Experience integrating AI into software products
- Cloud architecture experience (containers, orchestration, scaling)
- Working knowledge of SAP S/4HANA Private Cloud and clean core extension patterns
- Proventrack recorddesigning integration between cloud-native applications and an ERP core
- High technical credibility with senior engineers in both cloud-native and SAP teamsPlus
- Experience with SAP BTP and SAP CAP (Node.js/CDS)
- Experience with RISE with SAP orhyperscaler-hosted SAP environments
- Exposure to SCM processes and SAP modules (SD, MM, EWM)
- Experience with ABAP / RAP at review levelechnology Stack
- Public Cloud & Product (≈70%)
Containers and orchestration (Kubernetes / Kyma), Cloud Foundry
Event-Driven Architecture, SAP Event Mesh, SAP Business Events
CI/CD, Infrastructure as Code, observability
- SAP Platform & Extensions (≈30%)
S/4HANA Private Cloud and Public Cloud side-by-side extensions
Clean Core OData APIs, ABAP / RAP (at review level)
- Data & AI
Generative AI / SAP AI Core, RAG, LLM integration
LangChain / LangGraph, agentic orchestration
- Infrastructure & Security
XSUAA / IAS / IPS, identity federation across hybrid landscapes
CI/CD (SAP and non-SAP toolchains)
AI Readiness
We expect our architects to work with AI, not just design for it. We assess AI readiness on two levels.- General AI Working Practice
Ability to judge where AI output is trustworthy and where it must be verified
Awareness of data protection, IP, and confidentiality boundaries when using AI tools
Willingness to share prompts, patterns, and lessons learned with the wider engineering organisation
- Role-Specific AI Usage
AI-assisted generation and maintenance of HLD, SDD, and TDD artefacts
Use of AI coding assistants for prototypes, spikes, and reference implementations
Designing AI-native product capabilities: RAG over product and SAP data, agentic workflows, guardrails and evaluation
Assessing AI features for cost, latency, and operational risk before they enter the architecture