AI Agent Engineer (Manufacturing)

As an AI Agent Engineer at our company, you will play a crucial role in designing and implementing production agentic AI systems for our manufacturing clients. Your responsibilities will revolve around leveraging AI technologies to optimize various aspects of manufacturing operations, such as knowledge assistants, quality control, maintenance workflows, and AI discovery roadmaps. You will primarily work with technologies like LangChain, LangGraph, MCP, Python, and RAG to deliver measurable operational wins and build reusable AI capabilities for future engagements. Key Responsibilities: - Discovery & Roadmap: - Assess current operations and data to identify high-ROI use cases - Deliver a prioritized 90-day roadmap including quick wins and structural builds - Agentic AI Development: - Build production agentic systems using LangChain and LangGraph - Design tool calling, MCP integrations, and guardrails against bad outputs - Establish RAG pipelines over manufacturing knowledge with proper evaluation harnesses - System Integration: - Integrate agents with MES, ERP, CMMS, and data platforms (Snowflake, Databricks) - Utilize Azure, AWS, or GCP along with APIs, webhooks, and integration tools where relevant - Ensure data access, governance, and PII/IP compliance - Testing & Go-Live: - Define success criteria, lead UAT, and deployment - Monitor accuracy, latency, and adoption; troubleshoot in production - Enablement & Reusable IP: - Document architectures, decisions, and runbooks for global collaboration - Contribute reusable templates and accelerators to the internal library - Stakeholder Partnership: - Collaborate with plant leaders, CIO/CDO teams, and operations, quality, and maintenance departments - Translate manufacturing needs into technical and delivery plans - Provide honest advice on AI automation and decision support Required Skills: - LangChain, LangGraph, and MCP - Strong Python skills, tool calling, and RAG production experience - Experience taking agentic or RAG systems to production with measurable outcomes - Awareness of smart factory concepts, MES, ERP, quality, and maintenance operations - Excellent communication skills for technical and business stakeholders - Ability to work effectively in global delivery models Preferred Experience: - 5+ years in software/AI engineering, with hands-on experience in agentic systems - Exposure to discrete/process manufacturing or similar operational domains - Knowledge of computer vision applications and cloud/data platforms - Familiarity with integration and automation tools and relevant certifications What Success Looks Like: - Adoption of production AI systems by operations teams - Improvement in key metrics like OEE, downtime, quality accuracy, etc. - Development of reusable accelerators for future projects - Establishment of high client trust and collaboration across teams If you are interested in this exciting opportunity, please apply here. Feel free to share this with any strong engineers who might be a good fit for this role. As an AI Agent Engineer at our company, you will play a crucial role in designing and implementing production agentic AI systems for our manufacturing clients. Your responsibilities will revolve around leveraging AI technologies to optimize various aspects of manufacturing operations, such as knowledge assistants, quality control, maintenance workflows, and AI discovery roadmaps. You will primarily work with technologies like LangChain, LangGraph, MCP, Python, and RAG to deliver measurable operational wins and build reusable AI capabilities for future engagements. Key Responsibilities: - Discovery & Roadmap: - Assess current operations and data to identify high-ROI use cases - Deliver a prioritized 90-day roadmap including quick wins and structural builds - Agentic AI Development: - Build production agentic systems using LangChain and LangGraph - Design tool calling, MCP integrations, and guardrails against bad outputs - Establish RAG pipelines over manufacturing knowledge with proper evaluation harnesses - System Integration: - Integrate agents with MES, ERP, CMMS, and data platforms (Snowflake, Databricks) - Utilize Azure, AWS, or GCP along with APIs, webhooks, and integration tools where relevant - Ensure data access, governance, and PII/IP compliance - Testing & Go-Live: - Define success criteria, lead UAT, and deployment - Monitor accuracy, latency, and adoption; troubleshoot in production - Enablement & Reusable IP: - Document architectures, decisions, and runbooks for global collaboration - Contribute reusable templates and accelerators to the internal library - Stakeholder Partnership: - Collaborate with plant leaders, CIO/CDO teams, and operations, quality, and maintenance departments - Translate manufacturing needs into tech