Description Title: AI Software Engineer
Location: Cartersville, GA (Qcells Cartersville Factory)
Department: Smart Intelligence Team
The AI Software Engineer will design, develop, and deploy AI systems across Qcells' solar manufacturing line - spanning ingot, wafer, cell, and module processes at the Cartersville (CTV) and Dalton (DLT) plants. The role covers two core areas: (1) deep learning-based machine vision for automated defect inspection and quality control, and (2) AI agent systems that combine large language models (LLM), retrieval-augmented generation (RAG), and real-time process monitoring to support frontline engineers. The engineer will own the full lifecycle from data collection and model training to production deployment and continuous improvement (MLOps).
Responsibilities - Build and maintain image datasets for inspection, including data collection, annotation workflows, and dataset versioning across cell and module processes
- Perform exploratory data analysis to extract statistics and actionable insights from vision inspection and process data
- Train and evaluate deep learning models for image classification, object detection, and instance segmentation (e.g., EL/AOI defect detection, over-kill and miss reduction)
- Develop advanced inspection architectures such as cascade (two-stage) inference for ambiguous defect verification
- Design and build AI agent systems using LLM and RAG pipelines (vector database, embeddings) for process Q&A, automated root-cause analysis (RCA), and real-time anomaly alerting (e.g., via messaging integrations such as Telegram)
- Deploy and integrate machine learning models into industrial equipment and inspection systems, including interfaces with manufacturing equipment, MES, and process databases
- Build and operate data pipelines connecting equipment, inspection images, and process parameters for correlation analysis and traceability
- Establish MLOps practices for model monitoring, retraining, and continuous performance improvement on newly generated production data
- Collaborate with process, equipment, and quality engineering teams to translate domain requirements into AI solutions
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
- Professional experience applying machine learning to computer vision - specifically image classification, object detection, and instance segmentation
- Proficiency with Python and deep learning frameworks (PyTorch, TensorFlow) and OpenCV
- Comfortable working across the entire machine learning lifecycle, from data gathering to model deployment
- Solid understanding of both classical computer vision algorithms and deep learning-based solutions
- Ability to collaborate effectively with colleagues, customers, suppliers, and stakeholders from different technical backgrounds
Preferred Qualifications - Experience building LLM / RAG-based applications or AI agents (e.g., vector databases, embedding models, prompt engineering, agent orchestration)
- Experience in a data-related role in a manufacturing industry
- Experience with network programming such as TCP/IP and SMB protocol
- Experience deploying and operating models in production (MLOps), including monitoring and retraining pipelines
- Familiarity with messaging or alerting integrations (e.g., Telegram, Slack) for operational notifications
