ESaaS - SAP - Technical - AI

Key Responsibilities

1. Data Ingestion & Transformation

• 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

2. SAP Integration & Data Extraction

• Develop integrations with SAP S/4HANA Public Cloud APIs
• Extract transactional and master data for reconciliation/comparison
• Handle authentication, API throttling, and data consistency

3. RAG-Based AI Solution Development

• 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)

4. AI Agent-Based Data Validation

• 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

5. Integration Failure Analysis (SAP CPI)

• 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

6. Azure-Based Application Development

• 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

7. Automation & Reporting

• Create automated dashboards:
• Failed records summary
• Root cause insights
• Data reconciliation status
• Integrate with Power BI (optional but preferred)

Required Skills & Experience

Technical Skills

• 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

Preferred Skills

• 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

Soft Skills

• Strong analytical & problem-solving skills
• Ability to translate business requirements into AI solutions
• Collaboration with SAP, Integration, and Data teams

Experience Level

• 5–10 years (with at least 2–3 years in AI/ML or advanced automation)

Deliverables

• 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

Nice-to-Have Enhancements (Optional Scope)

• Self-healing integration recommendations
• Chatbot interface for querying reconciliation issues
• Predictive failure detection using historical CPI logs

Suggested Role Tag (for hiring portals)

“AI + SAP Integration Engineer | RAG | Azure | CPI”

Key Responsibilities

1. Data Ingestion & Transformation

• 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

2. SAP Integration & Data Extraction

• Develop integrations with SAP S/4HANA Public Cloud APIs
• Extract transactional and master data for reconciliation/comparison
• Handle authentication, API throttling, and data consistency

3. RAG-Based AI Solution Development

• 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)

4. AI Agent-Based Data Validation

• 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

5. Integration Failure Analysis (SAP CPI)

• 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

6. Azure-Based Application Development

• 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

7. Automation & Reporting

• Create automated dashboards:
• Failed records summary
• Root cause insights
• Data reconciliation status
• Integrate with Power BI (optional but preferred)

Required Skills & Experience

Technical Skills

• 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

Preferred Skills

• 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

Soft Skills

• Strong analytical & problem-solving skills
• Ability to translate business requirements into AI solutions
• Collaboration with SAP, Integration, and Data teams

Experience Level

• 5–10 years (with at least 2–3 years in AI/ML or advanced automation)

Deliverables

• 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

Nice-to-Have Enhancements (Optional Scope)

• Self-healing integration recommendations
• Chatbot interface for querying reconciliation issues
• Predictive failure detection using historical CPI logs

Suggested Role Tag (for hiring portals)

“AI + SAP Integration Engineer | RAG | Azure | CPI”

Key Responsibilities

1. Data Ingestion & Transformation

• 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

2. SAP Integration & Data Extraction

• Develop integrations with SAP S/4HANA Public Cloud APIs
• Extract transactional and master data for reconciliation/comparison
• Handle authentication, API throttling, and data consistency

3. RAG-Based AI Solution Development

• 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)

4. AI Agent-Based Data Validation

• 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

5. Integration Failure Analysis (SAP CPI)

• 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

6. Azure-Based Application Development

• 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

7. Automation & Reporting

• Create automated dashboards:
• Failed records summary
• Root cause insights
• Data reconciliation status
• Integrate with Power BI (optional but preferred)

Required Skills & Experience

Technical Skills

• 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

Preferred Skills

• 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

Soft Skills

• Strong analytical & problem-solving skills
• Ability to translate business requirements into AI solutions
• Collaboration with SAP, Integration, and Data teams

Experience Level

• 5–10 years (with at least 2–3 years in AI/ML or advanced automation)

Deliverables

• 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

Nice-to-Have Enhancements (Optional Scope)

• Self-healing integration recommendations
• Chatbot interface for querying reconciliation issues
• Predictive failure detection using historical CPI logs

Suggested Role Tag (for hiring portals)

“AI + SAP Integration Engineer | RAG | Azure | CPI”