Key Responsibilities:
AI Strategy & Solution Design
• Lead end-to-end design and delivery of AI/ML solutions for manufacturing and ERP-integrated use cases including predictive maintenance, demand forecasting, quality control, and process automation.
• Architect and implement Generative AI solutions leveraging LLMs for intelligent document processing, chatbots, anomaly detection, and decision-support systems within ERP environments.
• Define AI use case roadmaps aligned to manufacturing business processes such as production planning (PP), materials management (MM), plant maintenance (PM), and sales & distribution (SD).
• Develop and validate ML models for structured data from ERP systems including SAP S/4HANA, ECC,leveraging Python-based frameworks (scikit-learn, TensorFlow, PyTorch, XGBoost).
• Integrate AI/ML outputs back into ERP workflows, dashboards, and reporting layers for real-time business impact.
AI Platform Architecture & ERP Integration
• Design scalable AI/ML architectures on cloud platforms (Azure, AWS) with connectors to SAP ECC SAP S/4HANA,
and Snowflake for end-to-end data pipelines feeding AI models.
• Build and maintain feature engineering pipelines ingesting manufacturing
data (IoT sensors, MES, SAP transactional data) for real-time and batch AI inference.
• Configure Retrieval-Augmented Generation (RAG) architectures, vector stores,
and LLM API integrations to enable intelligent Q&A over enterprise ERP and manufacturing knowledge bases.
• Develop and maintain MLOps pipelines using tools such as MLflow, Azure ML,
or SageMaker for model versioning, drift monitoring, and automated retraining.
AI Use Case Implementation & Delivery
• Lead end-to-end implementation of Predictive Maintenance, Cost Planning, Manufacturing Analytics, Production Planning AI, and Rolling Forecast processes within SAC.
• Configure driver-based planning models, allocation rules, distribution & spreading logic, and advanced formula-based calculations.
• Set up Planning Calendars, workflows, task assignments, and review/approval cycles (Schedule phase).
• Enable Predict capabilities: configure automated time-series forecasting (predictive scenarios) and planning propositions using SAC built-in ML features.
• Support ACT vs. BUD variance analysis, asymmetric reporting, and management reporting dashboards.
Development & Technical Implementation
• Develop AI-powered APIs and microservices using Python (FastAPI/Flask) and LangChain / RAG to expose ML model inference endpoints to ERP and manufacturing applications.
• Build Python-based ETL and feature engineering pipelines to transform raw SAP/ERP transactional data into AI-ready datasets stored in Snowflake or cloud data lakes.
• Create LangChain or similar G enAI orchestration workflows integrating LLMs with structured ERP data to power intelligent assistants and automated reporting tools.
• Perform data modeling and semantic layer design in SAP BW/4HANA or Snowflake to support AI feature stores and analytical use cases for manufacturing operations.
• Manage model governance, AI ethics guidelines, data access controls, and compliance requirements for AI deployments in regulated manufacturing environments.
Collaboration & Stakeholder Management
• Partner with Manufacturing, Operations, Supply Chain, and IT stakeholders to gather AI use case requirements, run discovery workshops, and translate business needs into scalable AI solutions.
• Conduct model validation, UAT, end-user training, and knowledge transfer to plant managers, operations analysts, and SAP functional teams.
• Collaborate with SAP functional consultants (FI/CO, MM, PP, SD) and data engineering teams on AI integration governance and model deployment cycles.
Prepare and present AI solution design documents, proof-of-concept demos, and business value assessments to senior stakeholders
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