AI/ML Software Engineer

• 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

Do you have a Bachelor's degree?, * 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, * 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