AI Engineer, SMAI
Micron Technology
- Location
- Fab 10A, Singapore
- Work model
- On-Site
- Level
- Mid
- H-1B history
- 69 approvals (FY2023)
- Posted
- Sep 10, 2026
Skills
About this role
Our vision is to transform how the world uses information to enrich life for all. Join an inclusive team passionate about one thing: using their expertise in the relentless pursuit of innovation for customers and partners. The solutions we build help make everything from virtual reality experiences to breakthroughs in neural networks possible. We do it all while committing to integrity, sustainability, and giving back to our communities. Because doing so can fuel the very innovation we are pursuing. AI Engineer, SMAI Job Summary Join Micron Technology at a defining moment in our AI and smart manufacturing transformation. As a Full-Stack AI Engineer, you will contribute to the design, development, deployment, and support of AI-powered applications that improve manufacturing operations across Micron's global network of fabs and assembly/test sites. You will work closely with data scientists, software engineers, and manufacturing stakeholders to build scalable AI solutions, web applications, APIs, and automation tools that enable data-driven decision-making and autonomous operations.
Main Responsibilities
Design and build end-to-end AI solutions, including data ingestion pipelines, feature engineering, model training and inference, APIs, web applications, and observability capabilities. Develop responsive front-end applications using React, Angular, or Streamlit, backed by Python/FastAPI services, with strong type-safe development and test coverage practices. Support development and implementation of machine learning and AI models for structured and unstructured data. Support development of analytics and AI solutions that improve manufacturing and operational decision-making. Support integration of LLMs and agentic AI capabilities into manufacturing and engineering workflows. Support integration of robotics platforms, AMRs, tool automation systems, control systems, and the Micron MES ecosystem. Develop and maintain containerized applications using Docker and deploy solutions through Kubernetes or OpenShift environments. Work with data platforms and modeling environments to support scalable AI application development. Other Responsibilities Contribute to CI/CD pipelines and automated software release processes using tools such as GitHub Actions, while supporting monitoring, drift detection, model evaluation, and feedback loop implementation for production AI applications. Apply Responsible AI practices, including input validation, prompt-injection prevention, PII/IP protection, model governance, and audit logging. Support application and model optimization efforts to improve performance, reliability, scalability, and cost efficiency. Support testing, debugging, technical documentation, installation, and maintenance activities 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. Minimum Required Qualifications / Experience Bachelor's or Master's degree in Computer Science, Software Engineering, Data Science, or a related field; equivalent industry experience accepted. 2+ years building and shipping production web or AI applications end to end. Experience developing and deploying web applications, APIs, or data-driven solutions. Hands-on experience with at least one cloud platform (AWS, Azure, or GCP). Must Have Technical Skills Strong programming skills in Python and modern JavaScript/TypeScript. Strong SQL proficiency and experience working with relational databases. Experience with Docker, Kubernetes/OpenShift, 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