Bachelor's degree in a quantitative discipline (e.g., Computer Science, Statistics, Industrial Engineering, Mathematics)
2+ years of relevant experience in software engineering, machine learning, or data engineering
Hands-on experience with LLM APIs, including tool/function calling and prompt design
Strong proficiency in Python, including backend services and REST APIs
Strong SQL skills and experience with relational databases
Experience deploying services on Linux, including troubleshooting and monitoring
Ability to collaborate with diverse technical teams and translate ambiguous requests into functional tools
Responsibilities
Design and develop LLM-based AI agents for various manufacturing domains
Build agent tools for safe access to manufacturing data with SQL guardrails
Develop real-time monitoring agents to detect anomalies and deliver alerts
Create automated reporting workflows for generating production reports and analyses
Manage LLM serving and routing across cloud APIs and local GPU servers
Build domain knowledge bases from SOPs and engineering reports
Develop and deploy ML models for failure prediction and anomaly detection
Establish quality controls for agents, including answer verification and user feedback loops
Integrate agents with factory systems like digital twin platforms and dashboards
Collaborate with engineers to turn field requests into agent features
Benefits
Opportunity to work in a cutting-edge AI environment
Engagement with a diverse team of engineers and stakeholders
Hands-on experience with advanced AI technologies
Potential for career growth in a rapidly evolving field
Contribution to innovative solutions in solar manufacturing
Full Job Description
Description
AI Software Engineer (AI Agent Development)
Title: AI Software Engineer (AI Agent Development)
Location: Cartersville, GA (Qcells Cartersville Factory)
Department: Smart Intelligence Team
The AI Software Engineer will design, build, and operate AI agent systems that support engineers and managers across Qcells' solar manufacturing lines (ingot, wafer, cell, and module) at the Cartersville (CTV) and Dalton (DLT) plants. The team runs a production multi-agent platform in which domain agents answer process questions in natural language, query manufacturing data through tools, monitor equipment in real time, send early-warning alerts, and generate reports for engineering and management. This role focuses on agent development: tool design, data integration, LLM orchestration, evaluation, and reliable 24/7 operation on the factory floor. Machine learning models built on vision and process data are developed and served as tools that the agents use.
Responsibilities
Design and develop LLM-based AI agents for manufacturing domains (ingot, wafer, cell, module), including tool/function calling, multi-step reasoning, intent routing, and conversation context
Build agent tools that safely access manufacturing data (MES, Oracle/MariaDB process databases, REST data gateways, inspection image stores), with SQL guardrails, query validation, and access controls
Develop real-time monitoring and alerting agents that detect equipment and process anomalies, attach evidence (charts, images, video), and deliver actionable alerts to engineering chat rooms (e.g., Telegram, Microsoft Teams)
Build automated reporting and analysis workflows in which agents generate daily/weekly reports, root-cause analyses (RCA), and documents or slides from live production data
Manage LLM serving and routing across cloud APIs (e.g., Claude) and on-premise open-weight models on local GPU servers, including prompt management, prompt caching, and cost/latency optimization
Build domain knowledge bases and retrieval (RAG) from SOPs, engineering reports, and team conversations
Develop and deploy ML models that agents use as tools, such as image-based failure prediction, defect classification, and anomaly detection, with automated retraining and performance monitoring
Establish evaluation and quality controls for agents: answer verification, user feedback loops, regression tests, and guardrails against incorrect or fabricated answers
Integrate agents with factory systems such as digital twin platforms, web dashboards, and production simulators
Work directly with process, equipment, and quality engineers to turn field requests into agent features, and own those features in production
Required Qualifications
Bachelor's degree in a quantitative discipline (e.g., Computer Science, Statistics, Industrial Engineering, Mathematics, or a related field)
2+ years of relevant experience in software engineering, machine learning, or data engineering
Hands-on experience building applications on LLM APIs, including tool/function calling, structured outputs, and prompt design
Strong proficiency in Python, including backend services, asynchronous programming, and REST APIs
Strong SQL skills and experience working with relational databases
Experience deploying and operating services on Linux (scheduling, logging, monitoring, troubleshooting)
Ability to collaborate effectively with colleagues, customers, suppliers, and stakeholders from different technical backgrounds, and to turn ambiguous requests into working tools
Preferred Qualifications
Professional working proficiency in Korean (speaking, reading, and writing) to collaborate with Korean-speaking engineers and headquarters teams and to build agents that serve Korean-language users
Experience with agent frameworks and protocols (e.g., Claude Agent SDK, LangGraph, Model Context Protocol)
Experience with RAG systems, vector databases, and embedding models
Experience serving open-weight LLMs on premise (e.g., vLLM, Ollama) and managing GPU servers
Experience with computer vision or time-series machine learning (PyTorch) and MLOps practices
Experience with manufacturing data (MES, equipment logs, inspection images), especially in solar, semiconductor, or display industries
Experience with messaging bot integrations (e.g., Telegram, Slack, Microsoft Teams) and web dashboards
