Role Description Serve as the engineering lead for Agentic AI delivery across Supply Planning and Manufacturing — owning the design, development, and deployment of production-grade AI agent solutions. - Architect and build multi-agent AI systems using Azure AI Agent Service, AutoGen, Semantic Kernel, and/or LangChain/LangGraph — including orchestrator-executor patterns, tool calling, memory management, and agent coordination. - Implement the MCP to surface enterprise data as structured context for AI agents operating in supply chain and manufacturing workflows. - Build and deploy generative AI solutions on Azure AI Foundry — RAG-based knowledge agents, decision support for forecasting and capacity planning, and document intelligence for maintenance work orders and recipes. - Design and deliver AI copilots and topic-based agents using Microsoft Copilot Studio — enabling Supply Planning and Manufacturing teams to access insights and take action directly from Teams and Outlook. - Act as the AI delivery owner for agentic use cases — scoping business problems with stakeholders, defining agent capabilities and tool surfaces, prioritizing the roadmap, and driving adoption. - Apply emerging agentic AI patterns — including ReAct, Plan-and-Execute, reflection, and human-in-the-loop — for supply chain and operational use cases. - Partner with Supply Chain leadership, Demand Planning, Process Engineering, Maintenance Ops, and Plant teams to identify, scope, and deliver AI use cases that influence operational decisions. - Define and maintain AI agent governance — prompt versioning, tool auditing, evaluation frameworks, observability, and safety guardrails for production deployments. - Develop on Azure Databricks — PySpark and SQL against gold/platinum Delta tables, notebooks for transformation and feature work, and orchestration via Workflows. - Build and maintain Power BI reports and semantic models that serve as grounding data for AI agents and executive dashboards across Supply Planning and Manufacturing. - Own Supply Chain AI metrics alignment cadence — keeping priorities, status, and roadblocks visible to Supply Chain and Manufacturing leadership. - Mentor analysts and engineers on agentic AI design patterns, MCP, and AI delivery best practices. Qualifications - Master’s degree in Mathematics, Computer Science, Data Science, Information Systems, Engineering, or a related field with 5+ years of relevant analytics / AI experience, OR - Bachelor’s degree in Chemical, Industrial, Computer Science, or related fields with 8+ years of relevant analytics / AI experience. Requirements - Hands-on experience with the MCP — building or consuming MCP servers/clients; ability to expose enterprise data sources (databases, APIs, SharePoint, ERP) as MCP tools for AI agents. - Hands-on experience with multi-agent system design — designing and implementing multi-agent architectures; orchestrator-executor patterns, tool calling, memory management, and agent coordination using AutoGen, Semantic Kernel, LangChain/LangGraph, or Azure AI Agent Service. - Strong Python engineering skills — building production-grade AI agents and pipelines, including REST API integration, prompt versioning, evaluation frameworks, and observability for LLM-based systems. - Compulsory — must have hands-on experience with two or more of the following: - Azure AI Foundry (RAG pipelines, prompt flows, agent service) - Microsoft Copilot Studio (agents, topics, actions, Power Automate integration) - Microsoft 365 Copilot extensibility (plugins, connectors, Graph APIs) - Microsoft Power BI (DAX, semantic modeling, performance tuning) - Strong proficiency in Databricks (Python, SQL, Delta Lake, PySpark, notebooks). - Strong functional understanding of Supply Planning (S&OP, demand/supply planning, inventory, order management) and/or Manufacturing (plant maintenance, capacity planning, OEE). - Experience with SAP ECC / S/4HANA supply chain and manufacturing modules (MM, PP, SD, PM). - Ability to translate business problems into agentic AI solutions and communicate clearly to technical and executive audiences. - Strong collaboration and stakeholder management skills in cross-functional environments. Preferred Qualifications - Experience deploying AI agents in production — evaluation frameworks, safety guardrails, logging, and human-in-the-loop workflows. - Familiarity with agentic design patterns (ReAct, Plan-and-Execute, reflection, structured tool outputs). - Familiarity with knowledge graphs or graph databases (e.g., Neo4j) for agent reasoning and grounding. - Strong Power BI experience — semantic modeling, performance optimization, executive dashboard design. - Experience in chemicals, manufacturing, or process industries. - Experience with Palantir Foundry (pipelines, ontology, Workshop, AIP). - Exposure to MLOps on Azure (Azure ML, MLflow, Databricks Asset Bundles, CI/CD for analytics). - Experience designing operational KPI frameworks (MAPE, OTIF, service level, OEE, downtime). - Experience with containerization (Docker), version control (Git), and modern software engineering practices. Other We are an Equal Opportunity, Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to gender, pregnancy, race, national origin, religion, age, sexual orientation, gender identity, veteran or military status, status as a qualified individual with a disability or any other characteristic protected by law. To be considered for this position candidates are required to submit an application for employment through our career site and, be at least 18 years of age. Any offer of employment will be conditioned upon successful completion of a drug test and background investigation, as well as authorization for the Company to conduct additional periodic background checks as required by the Chemical Facility Anti-Terrorism Standards (CFATS) or regulations adopted by the department of Homeland Security or other regulatory agencies. A prior criminal record is not an automatic bar to employment, and the Company will conduct an individualized assessment and reassessment, consistent with applicable law, prior to making any final employment decision.