AI/ML Specialist - SAP CX Business AI COE - 12+ years

Job description

**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. Meet your team We are looking for a strong AI/ML Specialist to bring deep artificial intelligence expertise into the SAP Business AI COE. This role is meant to complement, not replace, SAP functional and technical expertise. The candidate does not need to be an SAP expert on day one, but must be capable of working with SAP consultants, architects, and customer stakeholders to design reliable, scalable, and governed AI-led solutions. The role will focus on translating business problems into appropriate AI solution patterns across Generative AI, classical ML, RAG, agentic workflows, document intelligence, voice AI, recommendation, prediction, and automation use cases. The person should help the team move beyond demos and build production-grade AI solutions with measurable quality, guardrails, and business impact. What you’ll do - SAP consultants bring business process, customer landscape, integration, and delivery context. A pure AI specialist brings depth in model behavior, data science, evaluation, hallucination control, RAG quality, agent design, and responsible AI. - SAP context can be learned with guidance, but deep AI engineering maturity is harder to build quickly through tool enablement alone. - This role will help the COE avoid building only “AI-wrapped SAP demos” and instead create reusable, enterprise-grade AI assets that can be tested, governed, improved, and scaled. Expected Outcomes - Design AI-led architectures aligned to business outcomes and SAP-centric processes. - Establish reusable AI patterns for customer support agents, quote creation agents, document intelligence, voice agents, knowledge assistants, and internal productivity tools. - Define evaluation frameworks to measure accuracy, retrieval quality, consistency, confidence, and business value. - Improve solution reliability through guardrails, human-in-the-loop design, observability, test datasets, and feedback loops. - Coach SAP consultants on when to use GenAI, classical ML, automation, RAG, fine-tuning, or agentic orchestration. Key Responsibilities - AI Solution Architecture and Design - Architect AI solutions that enhance business processes, decision-making, automation, and customer experience. - Translate business requirements into AI solution patterns across SAP and non-SAP landscapes. - Define end-to-end technical designs covering data flow, model selection, orchestration, integration, observability, and human oversight. - Partner with SAP CX, BTP, integration, and functional consultants to embed AI into enterprise workflows. - Generative AI, RAG, and Agentic AI Development - Design and build LLM-based applications using prompt engineering, tool calling, function calling, RAG, agent workflows, memory, and guardrails. - Define when to use prompting, retrieval, fine-tuning, classical ML, workflow automation, or human approval. - Work with vector databases, embeddings, chunking strategies, retrieval pipelines, reranking, and grounding techniques. - Develop agentic workflows that can reason, retrieve, call tools/APIs, execute tasks, and escalate safely when needed. - AI Quality, Evaluation, and Continuous Improvement - Create evaluation frameworks for AI outputs using golden datasets, human review, automated scoring, regression tests, and business KPIs. - Measure and improve accuracy, hallucination rate, retrieval relevance, completeness, consistency, confidence scoring, and task success rate. - Design feedback loops for continuous learning, prompt improvement, knowledge updates, and quality governance. - Set up experimentation practices to compare models, prompts, retrieval approaches, and agent designs objectively. - Machine Learning and Data Science - Apply classical ML techniques where GenAI is not the right fit, including classification, forecasting, clustering, anomaly detection, recommendation, sentiment analysis, and entity extraction. - Work with structured and unstructured data to identify patterns, build features, train models, and validate outcomes. - Support use cases involving predictive insights, prioritization, matching, routing, recommendation, and decision support. - Enterprise Integration and SAP Business AI Alignment - Collaborate with SAP teams to integrate AI capabilities with SAP CX, SAP Business Suite, SAP BTP, APIs, microservices, and enterprise data sources. - Leverage or align with services such as SAP Generative AI Hub, SAP AI Core, SAP AI Launchpad, SAP HANA Cloud vector capabilities, SAP Integration Suite, and relevant BTP services where applicable. - Design AI solutions that can work across SAP and non-SAP systems through secure APIs, event-driven flows, and reusable service patterns. - Stay current with SAP Business AI, Joule, AI agents, A2A/MCP patterns, and emerging enterprise AI architecture practices. - Governance, Security, and Responsible AI - Ensure AI solutions follow enterprise standards for data privacy, security, access control, auditability, explainability, and responsible AI. - Define guardrails for sensitive data handling, prompt injection risk, hallucination management, bias checks, and controlled tool execution. - Document model assumptions, evaluation results, known limitations, fallback behavior, and human oversight mechanisms. - Technical Leadership and Enablement - Provide AI technical leadership to project teams during discovery, solutioning, prototyping, delivery, and production hardening. - Mentor SAP consultants a