AI Agent Engineer (Manufacturing)

TalentXM (Formerly BlockTXM Inc); India; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

TalentXM is seeking an AI Agent Engineer to design and ship production agentic AI systems for manufacturing clients, leading engagements with LangChain, LangGraph, MCP, Python and RAG to build knowledge assistants over SOPs, maintenance logs and manuals.

The role emphasizes measurable outcomes like improved OEE, reduced downtime and quality improvements, with roadmaps, MES/ERP/CMMS integrations, and documentation for async collaboration across global pods.

Qualifications

• LangChain, LangGraph, and MCP (Model Context Protocol).
• Strong Python, tool calling, and 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

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 builds)
• 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)
• 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

Skills

LangChain/LangGraph/MCP

Python

Tool calling

RAG production

Industry 4.0 awareness

Communication (written)

Async documentation

Global fractional pods

Tools

Azure

AWS

GCP

Databricks

Snowflake

Workato

n8n

Power Automate

MuleSoft

Boomi

Job description

AI Agent Engineer (Manufacturing AI - LangChain / LangGraph / RAG)

Role Overview

We are seeking an AI Agent Engineer 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 builds)
• 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)
• Strong Python, tool calling, and 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