AI Agent Engineer (Manufacturing)

AI Agent Engineer (Manufacturing AI — LangChain / LangGraph / RAG) Role Overview We are seeking an AI Agent Enginee r to design and ship production agentic AI systems for manufacturing clients. You will act as the builder and technical lead on engagements that put AI to work where it matters, knowledge assistants over SOPs, maintenance logs and equipment manuals; quality, maintenance and operations workflow agents; and AI discovery roadmaps that turn \"we should do something with AI\" into a funded plan. The core stack is LangChain, LangGraph, MCP, Python, and RAG.

This is a high impact role focused on delivering measurable operational wins OEE, downtime, scrap, quality accuracy, response time while building reusable AI capabilities the next engagement can stand on. Key Responsibilities Discovery & Roadmap Assess current operations, data, and AI readiness; identify high-ROI use cases Deliver a prioritized 90 day roadmap (quick wins plus structural build s) Agentic AI Development Build production agentic systems with LangChain and LangGraph Design tool calling, MCP integrations, and guardrails against bad outputs Stand up RAG pipelines over manufacturing knowledge (SOPs, work orders, manuals, issue history) with proper evaluation harnesses System Integration Integrate agents with MES, ERP, CMMS, and data platforms (Snowflake, Databricks) Build on Azure, AWS, or GCP; use APIs, webhooks, and integration tools (Workato, n8n, Power Automate) where relevant Handle data access, governance, and PII / IP responsibly Testing & Go-Live Define measurable success criteria; lead UAT and deployment Monitor accuracy, latency, and adoption; troubleshoot in production Enablement & Reusable IP Document architectures, decisions, assumptions, and runbooks in a way that supports async global collaboration Contribute reusable templates, prompts, demos, and accelerators to the TalentXM internal library Stakeholder Partnership Partner with plant leaders, CIO / CDO teams, operations, quality, and maintenance Translate manufacturing needs into clear technical and delivery plans Advise honestly on where AI should automate work, augment experts, or support a decision without overpromising Required Skills LangChain, LangGraph, and MCP (Model Context Protocol) Stro ng Pyt ho n, tool call ing, a nd RAG production experience, not just prototypes A track record of taking at least one agentic or RAG system to production with measurable outcomes Awareness of smart factory / Industry 4.0, OT/IT, MES, ERP, quality, maintenance, and production operations concepts Comfortable speaking with both technical teams and business stakeholders Strong written communication and async documentation Comfort working in global fractional pods and outcome based delivery models Preferred Experience 5+ years in software / AI engineering, with hands on agentic or LLM systems Experience across discrete and/or process manufacturing, or a comparable operational / regulated domain Computer vision exposure (visual inspection, defect detection) a plus Cloud and data platforms: Azure, AWS, GCP, Databricks, Snowflake Integration and automation: Workato, n8n, Power Automate, MuleSoft, Boomi Certifications a plus: AWS ML Specialty, Azure AI Engineer, Azure / AWS Solutions Architect, NVIDIA, Databricks, Snowflake, ISA CAP, or Workato Automation Pro. Equivalent hands on project experience accepted when backed by strong examples and references What Success Looks Like Production AI systems adopted by operations teams not stalled pilots Measurable movement on a real metric: OEE, downtime, scrap, quality accuracy, or response time Reusable accelerators that help the next pod deliver faster High client trust and strong cross pod collaboration Inter ested?

Apply here. Know a strong engineer who would be a fit? Please share this with them.