Data Modeller

Key Responsibilities (Agentic Operations):

• AgentOrchestrated EndtoEnd Planning: Enable autonomous, crossfunctional supply chain planning that synchronizes demand, supply, manufacturing, and fulfillment to customer outcomes.
• Autonomous Performance & Loss Management: Deploy AI agents to continuously monitor service, cost, and fulfillment signals, proactively eradicating losses and improving plan adherence.
• Intelligent Manufacturing & Capacity Optimization: Use agentic insights to analyze production constraints, expedite plans, optimize capacity utilization, and ensure on time production performance.
• RealTime Disruption Response: Leverage AI to sense and respond to internal and external disruptions by dynamically rerouting logistics and rebalancing inventory to mitigate risk.
• GoalDriven Inventory & Replenishment: Shift from rulebased planning to autonomous, goalbased replenishment driven by forecasted demand, sales trends, service levels, and costtoserve.
• Agentic Procurement, Analytics & Data Foundation: Implement intelligent agents for sourcing and contract monitoring, enable agentassisted RCA and decision intelligence, and ensure highquality data integration across enterprise systems

Required Qualifications:

• Strong understanding of end-to-end supply chain (S&P, Logistics, Fulfillment).
• Experience in managing AI agents, machine learning, or autonomous systems.
• Knowledge of Python, SQL, or RPA tools is a plus.
• Candidate should know cloud deployment (Preferred Azure platform) migrate python developed codes to Azure and host it
• Able to build AI agents. (Note: They should be capable to build AI Agents)
• Experience on the AI concepts such as LLM's, RAG, etc etc and lead the team in \