Senior Full-Stack AI Engineer, Ipai, Smai

Job Summary JoinMicron's AI Revolution in Smart Manufacturing Be part of agroundbreaking transformation as Micron accelerates the future of AI-drivenmanufacturing. As a Senior Full-Stack AI Engineer , you will architect,build, and deploy cutting-edge AI applications that power smarter, faster, andmore autonomous operations across Micron's global network of semiconductor fabsand assembly/test facilities. Working at theintersection of Artificial Intelligence, Software Engineering, and AdvancedManufacturing , you will collaborate with data scientists, engineers, andbusiness stakeholders to develop scalable machine learning solutions, modernweb applications, APIs, and intelligent automation platforms. Your work willdirectly influence how the world's most advanced memory and storage productsare manufactured, driving efficiency, quality, and innovation at a globalscale. MainResponsibilities Design and build end-to-end AI solutions, including data ingestion pipelines, feature engineering, model training and inference, APIs, web applications, and observability capabilities. Architect and develop scalable full-stack AI applications using modern web technologies, cloud-native platforms, and distributed computing frameworks, while promoting strong type-safe development and test coverage practices. Develop, implement, and optimize machine learning, deep learning, and statistical models for structured and unstructured data. Develop and communicate descriptive, diagnostic, predictive, and prescriptive analytics to support manufacturing and operational decision-making. Design and integrate LLMs and agentic AI capabilities into production workflows for manufacturing and engineering users. Lead technical integration of robotics platforms, AMRs, tool automation systems, control systems, and the Micron MES ecosystem. Drive deployment, scalability, and reliability of production AI applications using Docker, Kubernetes, and OpenShift across global manufacturing environments. Work with large-scale computing frameworks, data platforms, and modeling environments to deliver production-grade AI solutions. OtherResponsibilities Drive best practices for CI/CD, automated software releases using GitHub Actions, monitoring, model evaluation, drift detection, and feedback loop implementation for production AI applications. Apply Responsible AI practices, including security, governance, validation, auditability, and compliance requirements. Lead application and model optimization initiatives to improve performance, scalability, reliability, and cost efficiency. Provide technical mentorship, code reviews, and knowledge sharing across engineering teams. Lead testing strategies, debugging efforts, and technical documentation for AI and software solutions. Integrate AI-assisted tools and insights into daily work to improve efficiency, quality, and effectiveness while complying with organizational standards, legal requirements, and governance policies. Contribute to a culture of continuous improvement by identifying, testing, and sharing AI-enabled enhancements within one's scope of work. MinimumRequired Qualifications / Experience Bachelor's or Master's degree in Computer Science, Software Engineering, Data Science, Machine Learning, Statistics, or a related field; equivalent industry experience accepted. 5+ years building and shipping production software, machine learning, or AI applications. Experience developing and deploying web applications, APIs, machine learning models, or data-driven solutions. Experience working with cloud platforms, modern software engineering practices, and large-scale data environments. Must HaveTechnical Skills Strong programming skills in Python and modern JavaScript/TypeScript. Strong SQL proficiency and experience working with structured and unstructured data. Experience with machine learning, deep learning, statistical modeling, and predictive analytics techniques. Experience with Docker, Kubernetes/OpenShift, cloud platforms, and CI/CD development practices. Working knowledge of GenAI technologies, including prompt engineering, Retrieval-Augmented Generation (RAG), LLM integration, agentic AI concepts, model evaluation, and Responsible AI practices. Experience working with large-scale computing frameworks, distributed data processing, or enterprise-scale AI platforms. Ability to apply AI literacy and digital fluency to use AI-enabled tools responsibly and effectively for research, analysis, content creation, problem…