AI/ML Software Engineer

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