Lead Agentic AI Engineer-US Remote - US-OH,Columbus

Job Responsibilities

• 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.

Minimum Qualifications

• Education & Experience (one of the following):
• 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.
• Technical:
• 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.