- Design pipelines to:
- Ingest Excel files from shared folders (SharePoint/Network/Azure storage)
- Convert structured/unstructured data into RAG-ready vector datasets
Implement data preprocessing, normalization, and metadata tagging
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- Develop integrations with SAP S/4HANA Public Cloud APIs
- Extract transactional and master data for reconciliation/comparison
Handle authentication, API throttling, and data consistency
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- Build and deploy RAG pipelines using Azure AI services
- Enable:
- Semantic search
- Context-aware data retrieval
- Knowledge grounding from Excel + SAP data
Maintain vector databases (e.g., Azure AI Search / embeddings store)
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- Design AI agents to:
- Compare SAP vs Excel data
- Identify mismatches, missing records, inconsistencies
- Automate reasoning workflows for:
- Data validation
- Exception identification
Auto-suggestions for missing data
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- Parse and analyse SAP CPI logs
- Correlate failed integration records with:
- Missing/incorrect data
- Mapping errors
- API failures
Build AI-assisted root cause classification engine
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- Develop and deploy applications on Microsoft Azure
- Use:
- Azure Functions / App Services
- Azure AI / OpenAI services
- Azure Data Factory / Logic Apps
Ensure scalability, security, and monitoring
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- Create automated dashboards:
- Failed records summary
- Root cause insights
- Data reconciliation status
Integrate with Power BI (optional but preferred)
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- Strong experience in:
- Python / AI development
- RAG architectures & LLM integration
- Hands-on with:
- Azure AI services / Azure OpenAI
- Vector databases (Azure AI Search / FAISS / etc.)
- SAP expertise:
- SAP S/4HANA Public Cloud APIs
- SAP CPI (Cloud Platform Integration)
- Data handling:
- Excel processing, ETL pipelines
API integration & JSON/XML handling
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- Experience with:
- AI Agents / Autonomous workflows
- LangChain / Semantic Kernel / similar frameworks
- Data quality and reconciliation solutions
- Knowledge of:
- SAP Datasphere / BTP ecosystem
Exposure to enterprise integration patterns
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- Strong analytical & problem-solving skills
- Ability to translate business requirements into AI solutions
Collaboration with SAP, Integration, and Data teams
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5–10 years (with at least 2–3 years in AI/ML or advanced automation)
- End-to-end AI solution for:
Data ingestion RAG conversion SAP extraction AI comparison- CPI failure analysis
- Production-ready deployment on Azure
Automated reporting & monitoring framework
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- Self-healing integration recommendations
- Chatbot interface for querying reconciliation issues
Predictive failure detection using historical CPI logs
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“AI + SAP Integration Engineer | RAG | Azure | CPI”
