Staff/Principal Full-Stack AI Engineer, Ipai, Smai

Job Summary Lead the Future of AI-Powered Manufacturing at Micron Micron is accelerating its transformation toward Autonomous Operations through Artificial Intelligence, advanced analytics, digital twins, robotics, and intelligent automation. As a Staff/Principal Full-Stack AI Engineer , you will be a technical leader driving the strategy, architecture, and delivery of next-generation AI products that power smarter manufacturing across Micron's global network of fabs and assembly/test sites. In this highly influential role, you will own AI solutions end-to-end—from data engineering, machine learning, and agentic AI systems to modern full-stack applications, digital twin platforms, robotics integration, and edge-to-cloud deployment. Working closely with data scientists, software engineers, robotics teams, and manufacturing leaders, you will transform innovative ideas into scalable, production-grade solutions that improve efficiency, increase automation, and enable intelligent decision-making at enterprise scale. This is a unique opportunity to shape the future of AI in semiconductor manufacturing and directly contribute to Micron's vision of autonomous, data-driven operations worldwide. Main Responsibilities Design, architect, and build end-to-end AI products, including data ingestion pipelines, feature engineering, model training and inference, APIs, web applications, and observability frameworks. Develop responsive front-end applications using React, Angular, or Streamlit, backed by Python/FastAPI services, with strong type-safe development and test coverage practices. Architect and maintain scalable, modular application layers that bridge real-time sensor, robotics, and manufacturing data with enterprise-facing dashboards and decision-support systems. Design and develop full-stack integration of robotics platforms, Autonomous Mobile Robots (AMRs), tool automation systems, and control systems with smart manufacturing applications and the Micron smart manufacturing ecosystem. Design and implement digital twin environments, including 3D representations of fab layouts, AMR fleets, and tool stations using platforms such as NVIDIA Omniverse, Gazebo, Unity Robotics Hub, or equivalent technologies. Interface with Smart Manufacturing systems to integrate structured operational data into AI pipelines and digital twin environments. Design and integrate LLMs and agentic AI frameworks into production workflows for manufacturing and engineering users. Develop AI-assisted robotics orchestration capabilities and 3D-aware AI features that leverage spatial data, equipment positioning, robot trajectories, and fab maps to improve anomaly detection, route optimization, and predictive maintenance. Containerize AI and robotics services using Docker and deploy solutions through Kubernetes, OpenShift, and modern cloud-native platforms. Package and deploy robotics software stacks, AI services, and 3D rendering workloads as containerized microservices supporting cloud, on-premises, and edge manufacturing environments. Manage GPU-accelerated inference workloads and real-time simulation environments supporting AI, robotics, and digital twin applications. Optimize AI application performance and cost through caching, batching, GPU utilization, model quantization, and inference optimization strategies. Validate digital twin and simulation scenarios against real-world operational constraints before production deployment to support safe and reliable autonomous operations. Other Responsibilities Lead implementation of CI/CD, automated software releases, monitoring, model evaluation, drift detection, and AI application lifecycle management for production AI applications. Apply Responsible AI practices, including input validation, prompt-injection defense, PII/IP protection, model governance, auditability, and compliance requirements. Design and implement improvements to AI platform scalability, reliability, maintainability, performance, and cost efficiency. Develop and communicate descriptive, diagnostic, predictive, and prescriptive analytics to support manufacturing and operational decision-making. Develop, implement, and optimize machine learning, deep learning, and statistical models for structured and unstructured data. Perform code reviews, provide technical mentorship, and drive software engineering best practices across project teams. Lead testing, debugging, technical documentation, installation procedures, and maintenance strategies for …