Expert Machine Learning Engineer (Agentic AI, SAP BTP)

About the job

We help the world run better
At SAP, we keep it simple: you bring your best to us, and we'll bring out the best in you. We're builders touching over 20 industries and 80% of global commerce, and we need your unique talents to help shape what's next. The work is challenging – but it matters. You'll find a place where you can be yourself, prioritize your wellbeing, and truly belong. What's in it for you? Constant learning, skill growth, great benefits, and a team that wants you to grow and succeed.

PURPOSE AND OBJECTIVES

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.

What You Will Do

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.

What You Bring

Experience & 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 (Strong - Non-Negotiable)

• 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: Strong 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.

Technical Expertise

AI/ML & GenAI:

• Hands-on experience with LLMs, foundation models, Agentic AI, and deep learning.
• Strong applied skills in prompt engineering, RAG, AI agents/multi-agent systems, embeddings, semantic search, fine-tuning, model evaluation, and guardrails.
• Experience building and owning evaluation frameworks for AI systems, including domain-specific validation of LLM outputs.

Frameworks & Tools:

• LangChain, LlamaIndex, Semantic Kernel, Hugging Face, OpenAI APIs.
• Vector databases: Pinecone, Weaviate, ChromaDB, FAISS, or HANA Cloud Vector Engine.
• TensorFlow, PyTorch, scikit-learn.

Architecture & Platforms:

• Distributed systems, microservices/APIs, scalable application design with resiliency and fault tolerance.
• Cloud AI platforms: SAP BTP (primary), AWS, Azure, or GCP.
• Kubernetes/Docker, CI/CD for AI systems.

Data Engineering:

• Spark/Databricks, data pipeline design, and big-data ecosystems.
• HANA Cloud query design and optimization.

SAP Ecosystem Knowledge

• Hands-on experience with SAP AI Core and the SAP Generative AI Hub.
• Practical experience with CAP (Cloud Application Programming Model) in Java or Node.js.
• Understanding of SAP BTP services, multi-tenancy patterns (MTX), IAS/AMS.
• Knowledge of SAP Business Data Cloud (BDC) - data products, object store, Delta Sharing, semantic layer - is a distinct advantage.

Preferred Qualifications

• Open-source contributions, publications, or patent filings.
• Hands-on experience in GenAI transformation, enterprise search, copilots, or autonomous agent frameworks.
• Strong understanding of Responsible AI, governance, and AI security.
• Experience with AI cost management and performance benchmarking in production environments.