SAP Business Network is moving beyond simple automation to build autonomous supply chain agents capable of reasoning, planning, and exception handling, requiring architecture that bridges unstructured data with structured business logic using Generative AI.
Requirements
- 5+ years of experience in designing and architecting large-scale distributed systems or cloud-native applications (e.g. on AWS, GCP, Azure, or SAP BTP)
- 5+ years of technical leadership (e.g. Staff Engineer, Tech Lead, or Architect roles) driving technical decisions
- 5+ years of applied AI experience (e.g. NLP, Machine Learning) including 1+ years of experience with Generative AI techniques like Large Language Models, RAG, Agentic Workflows
- Deep understanding of Neurosymbolic AI, Reasoning Engines, and the ability to ground LLM outputs using formal logic or constraints
- Experience designing and implementing Knowledge Graphs (RDF, Property Graphs) to power retrieval (GraphRAG) and reasoning systems
- Hands-on experience with agentic tools (e.g. LangChain, LangGraph), prompt engineering, and context management
Responsibilities
- Lead the technical architecture for high-scale AI applications
- Design and implement multi-agent systems capable of autonomous reasoning, planning and execution.
- Actively contribute to the codebase of critical components and complex architectural patterns.
- Build greenfield Proofs of Concept (PoCs) and guide them to production, setting the standard for code quality and MLOps best practices.
- Partner with Product Management to assess technical feasibility and translate complex business requirements into robust technical designs.
- Mentor Senior and Specialist developers, conduct design and code reviews and foster a culture of engineering excellence.
Other
- This is a hybrid role based out of Palo Alto, CA. Hybrid is 3 days a week on-site, 2 days a week remote.
- Bachelor’s degree in Computer Science, Engineering or a related technical field
- 10+ years of professional experience in software development
- Familiarity with Supply Chain Management, procurement processes, or logistics data models
- Expected Travel: 0 - 10%
