Responsibilities
- Create sophisticated machine learning, statistical, and optimization techniques to address intricate business and technical challenges
- Apply a comprehensive data science methodology encompassing problem definition, feature creation, model development, validation, and impact assessment
- Analyze large volumes of data—including process, yield, test, and operational datasets—to generate practical insights
- Incorporate domain expertise in semiconductor and NAND technologies, including process limitations and variability, into model development and analysis
- Design and implement generative AI solutions such as large language models, retrieval-augmented generation pipelines, and agent-based architectures for engineering applications
- Construct knowledge-based systems using enterprise data sources like documents, logs, and process records
- Develop evaluation methodologies to assess the quality, factual consistency, and dependability of generative AI outputs
- Utilize generative AI to enhance decision-making, automate processes, and improve operational efficiency at scale
- Design and deploy robust AI and machine learning systems, including data pipeline infrastructure and feature processing frameworks
- Build scalable platforms for model training and experimental iteration
- Implement inference systems capable of handling both real-time and batch processing demands
- Engineer systems that optimize trade-offs between prediction accuracy, scalability, response time, and cost
- Define integration strategies for embedding AI capabilities into current enterprise platforms and operational workflows
- Develop and manage MLOps pipelines covering continuous integration and deployment, model versioning, monitoring, and detection of data or concept drift
- Ensure AI systems are reproducible, observable, and compliant with governance standards
- Lead initiatives to transition models into stable, production-ready systems with long-term maintainability
- Collaborate with multidisciplinary teams across engineering, manufacturing, product development, and IT to convert business needs into technical solutions
- Guide data scientists and engineers in advanced modeling techniques, system architecture, and generative AI adoption
