AI Engineer

• 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.

• Build, train and deploy predictive machine learning models.

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.

  • Building and executing end-to-end ML systems automating training, testing, and deploying Machine Learning models in cloud platforms.