• Technical responsibility for SAP Basis architectures • Operation and ongoing development of complex SAP system landscapes • Management of projects and assumption of team leadership • Planning and implementation of transformations towards SAP S/4HANA • Integration of SAP systems into cloud platforms such as Microsoft Azure • Analysis of existing SAP landscapes and development of strategies

• Develop and architect AI agent systems based on single-agent and multi-agent architectures. • Implement modern communication protocols such as Model Context Protocol (MCP) and Agent-to-Agent (A2A). • Master the information lifecycle for AI: from strategies like chunking and embeddings to implementing techniques such as RAG (Retrieval-Augmented Generation), reranking, and query transformation. • Manage the complexity of multi-model environments. Evaluate and select the best providers based on trade-offs in latency, context, and cost. Standardize the use of abstraction frameworks to avoid vendor lock-in. • Act as a technical mediator in defining AI strategies across the product portfolio. • Ensure architectural consistency across initiatives, identifying opportunities for componentization and reuse. • Serve as a decision-maker in architectural dilemmas, promoting an AI Engineering culture. • Connect AI technical capabilities to the real pain points of business areas. Establish LLMOps pipelines that include: • Rapid experimentation cycles (A/B testing of prompts/models) • Feedback systems (human-in-the-loop) • Monitoring of business metrics impacted by AI • Systematic evaluation (Evals) - creation of test datasets and frameworks.

• Design and implement versioned HTTP APIs: OpenAPI/JSON Schema, authentication and authorization, validation, idempotency, and clear error models. • Build and run services on Azure: App Service or Functions, Storage, Service Bus or Event Grid, Key Vault, Managed Identity; apply VNet integration and private endpoints per architecture. • Deliver accessible front-end experiences; use a BFF or experience layer where it improves security and reduces inappropriate coupling between clients and domain services. • Integrate enterprise identity: OIDC/OAuth2, client credentials, token validation; follow zero-trust and least-privilege guidance from architects and security. • Consume Terraform or Bicep modules per Cloud Architect standards; avoid one-off infrastructure that drifts from platform norms. • Implement features on AWS when required (e.g. Lambda, API Gateway, S3, SQS, IAM) using prescribed patterns. • Instrument applications with structured logging, metrics, and tracing; meet testing and privacy expectations aligned with AFAs (for example observability, testing strategy, and zero-trust rules). • Contribute to hardening AFA Cursor rules, agent skills, and sub-agents for stack-specific patterns (e.g. API-first, Experience API, MCP) in collaboration with architects. • Produce handover inputs as versioned artifacts: ADRs, diagram-as-code, contract definitions in Git, and operational notes that receiving squads can adopt—not ad hoc documents outside version control. • Deliver early outcomes: at least one initiative increment shipped with contracts, automated tests, observability hooks, and reusable patterns in Git, plus AFA updates covering authentication, configuration, and environment promotion.

• Work in software development, writing code to build applications, programs, and software systems according to project specifications and requirements; • Test developed software, identifying and fixing issues, bugs, and defects in the code to ensure quality of delivery, meet deadlines, and achieve client satisfaction; • Document developed code to facilitate future maintenance and updates of products; • Develop high-quality software solutions, ensuring code is efficient, scalable, and maintainable; • Monitor and oversee systems in production to identify issues and implement solutions.