Job Summary

As a critical part of SAP's AI Transformation journey, the mission of the CPIT Order-to-Cash (O2C) Team is to drive the implementation of the company's strategy and achievement of shared business objectives through world class end-to-end business processes and systems. Amongst the team's key objectives are to innovate, standardize, simplify, and automate SAP's core business processes and assure continuous alignment with the SAP IT portfolio and business stakeholders. In all activities, closing the loop from strategy to execution is key to driving impact for SAP and for our customers.

About the Role

We are looking for an Expert AI/Agentic Engineer who combines deep hands-on engineering excellence with a strong architectural mindset. You will design, build, and operate enterprise-grade AI/ML and Generative AI solutions within the SAP ecosystem - spanning intelligent assistants, AI copilots, agentic workflows, and RAG-powered applications. You are first and foremost an engineer who writes production-quality code, and you bring the architectural thinking to ensure what you build scales, performs, and lasts.

You will work at the intersection of cutting-edge AI engineering and SAP's technology landscape, contributing to systems that deliver measurable business impact.

Responsibilities

AI/GenAI Engineering Architecture

- Design and build scalable LLM-powered applications, intelligent assistants, and AI copilots aligned to business transformation goals.

- Architect and implement advanced RAG pipelines, knowledge retrieval systems, and agentic / multi-agent workflows using commercial and open-source models.

- Write clean, well-tested, production-grade code across the full AI application stack - from data ingestion and embedding pipelines to orchestration logic, API layers, and evaluation harnesses.

- Integrate vector databases, semantic search, prompt orchestration, tool/function calling, memory, and context-aware reasoning into enterprise-grade solutions.

- Optimize prompts, inference pipelines, and evaluation frameworks; contribute to fine-tuning, alignment, and model optimization efforts.

- Independently design scalable,



AI-native application systems - moving quickly from ambiguity to working, deployable prototypes and iterating through execution.

SAP Ecosystem Integration

- Build agentic workflows on SAP AI Core and integrate LLM-based features into CAP /Fiori applications with strong coding standards and maintainability.

- Design and implement AI agents for enterprise domains - including formula explanation, computation assistance, and analytical workflows - using SAP AI Core and Python as the primary stack; CAP/Java as secondary.

- Contribute to the design of ontology-grounded agents that encode business rules, formula structures, and domain relationships as structured knowledge, enabling reliable agent reasoning beyond prompt-only approaches.

- Architect and deploy solutions on SAP BTP, with hands-on experience in multi-tenant SaaS patterns (MTX, service-manager, IAS/AMS).

- Knowledge of SAP Business Data Cloud (BDC) - including data products, HANA Data Lake, Delta Sharing, and the semantic layer - is a strong advantage.

Platform, LLMOps Observability

- Establish and implement LLMOps practices covering deployment, monitoring, observability, governance, and guardrails - with hands-on implementation, not just design.

- Instrument OTEL tracing across microservice fleets and build Cloud Logging dashboards for cross-service visibility.

- Optimize latency, throughput, and cost efficiency - including HANA Cloud query optimization and AI Core inference cost management.

- Apply SRE principles in practice: monitoring, alerting, incident response, and root cause analysis.

- Build and own evaluation and benchmarking frameworks that measure AI system quality, reliability,



and performance - including domain-specific test harnesses validating LLM outputs against known good results.

Research, Innovation Collaboration

- Contribute to PoCs, accelerators, and reusable AI solution ; actively participate in innovation incubation.

- Support publication of technical white papers and assets; contribute to patent ideation where applicable.

- Partner with platform, data, product, and business teams to design robust, production-ready solutions.

- Mentor junior engineers, conduct rigorous code reviews, and raise the engineering bar across the team.

Qualifications

Experience and Education

- 10-12 years of hands-on experience in AI/ML, Generative AI, Data Science, and/or Enterprise Architecture, with a proven track record of delivering production-grade LLM and AI solutions.

- Degree in Computer Science, AI, Data Science, Engineering, or a related field.

Coding Engineering Expertise

- Python (primary, expert-level): You write clean, modular, well-tested Python code for AI pipelines, agent frameworks, orchestration logic, APIs, and evaluation systems.

- Java / CAP (secondary): Comfortable integrating AI features into SAP CAP Java microservices; able to read, debug, and contribute to Java codebases confidently.

- SQL HANA Cloud: Solid SQL skills including query optimization, analytical functions, and working with HANA-specific constructs.

- You use AI coding tools to accelerate development but you read, challenge, and own every line - you catch bugs, design flaws, and wrong architectural calls in AI-generated code.

- Proficiency in building and consuming RESTful and event-driven APIs; strong understanding of asynchronous programming patterns.

- Experience with unit testing, integration testing, and test harness design for AI systems.

- Comfortable with version control (Git), CI/CD pipelines, code review practices, and engineering hygiene at scale.

Disclaimer: This job posting and Location has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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