SAP is looking to solve the business and technical problem of improving customer outcomes and internal productivity by developing and deploying AI capabilities.
Requirements
- Demonstrated success delivering LLM/RAG applications and internal APIs adopted across teams; comfortable owning features end-to-end in production.
- Advanced Python; strong API engineering (FastAPI or similar) and practical full-stack integration (e.g., React) when needed.
- Hands-on with LLMs (prompting, agentic workflows, retrieval), embeddings, and evaluation workflows; solid grounding in PyTorch/HF/scikit-learn and data tooling (Pandas, W&B).
- SQL (PostgreSQL) and streaming/batch data processing experience; Docker and Git for day-to-day DevEx.
- Experience deploying on one or more of Azure, GCP, or SAP BTP; familiarity with cloud-native services and microservice architectures.
- Hybrid search, metadata strategies, and rigorous offline/online evaluation harnesses for LLM systems.
- CI/CD for ML/LLM services; observability (metrics/logs/traces); basics of K8s and infrastructure-as-code (Terraform) are a plus, not a gate.
Responsibilities
- Own end-to-end delivery of AI capabilities that improve customer outcomes and internal productivity across Value Advisory, SAP Customer Success and SAP broadly.
- Lead solution design, prototype quickly, and productionize responsibly - spanning architecture, hands-on engineering, and cross-functional leadership (Product, CS Ops, Security/Compliance, platform teams).
- Translate ambiguous business problems into AI solution architectures, success metrics, and roadmaps; drive concept pilot production with light oversight.
- Design secure, reliable services for LLM/RAG, retrieval, and automation workflows; ship APIs and microservices with strong observability and maintainability.
- Select models/embeddings and vector storage; design chunking/indexing, prompting/tool use/agents; instrument evaluations and A/Bs to track quality and ROI.
- Containerize services, automate CI/CD, and partner with platform teams (SAP BTP/AI Core) for scale, cost efficiency, and enterprise compliance.
- Build integrations with SAP data/products (e.g., S/4HANA, SAP HANA Cloud/SAP Datasphere, SuccessFactors, CX) and CS tooling.
Other
- Constant learning, skill growth, great benefits, and a team that wants you to grow and succeed.
- Embed privacy, security, data governance, and auditability throughout the lifecycle.
- Mentor peers/interns, review designs/PRs, publish templates and SDKs that raise the engineering bar.
- Define KPIs/OKRs, instrument outcomes, and clearly communicate trade-offs to technical and non-technical stakeholders.
- Evidence of cross-functional work (product, stakeholders) and mentoring junior engineers/interns.
