Principal Software Engineer (AI)

Job Summary NetApp is building intelligent data infrastructure to help customers make their data AI-ready. The AI Business Unit is creating AI Data Services, connecting enterprise data with analytics and AI agents. As a Principal Engineer, you’ll help architect and shape this new platform from the ground up. You’ll lead complex technical initiatives across storage, data, and AI while remaining a hands-on individual contributor. It’s a unique opportunity to solve challenging data infrastructure problems and influence the future of AI at NetApp.

Key Responsibilities:

• Serve as the architectural north star for the system, resolving the hardest design, performance, and scaling problems as the team turns customer data into something analytically useful for people and AI agents.
• Design and tech-lead complex, cross-team initiatives, setting technical direction for products at the intersection of storage, data, and AI.
• Drive high-leverage technical decisions on data architecture, including technology choices, data modeling, and system scalability and reliability, across both on-premises and cloud deployments.

Required Qualifications:

• 10+ years of hands-on software engineering experience, including technical lead or primary design owner experience on distributed systems and data platforms.
• Deep hands-on experience in Python, Java, or a comparable modern language, building production-grade data pipelines and services.
• Hands-on experience with agentic AI engineering, or AI/agent-facing infrastructure such as retrieval, search, or tool-access protocols for AI applications.
• Strong understanding of modern data platform architectures, including data lakes, table formats, and Iceberg-based (or comparable) catalog systems.
• Exposure to large-scale distributed systems, including streaming or event-driven architectures.
• Hands-on experience with cloud-native architectures, and familiarity with on-premises infrastructure and storage systems.
• Experience with distributed query engines and SQL-based data access patterns.
• Demonstrated ability to drive technical decisions and influence direction across multiple engineering teams without relying on formal management authority.

Education:

Bachelor’s degree in Computer Science, Engineering, or a related field required. Equivalent practical experience will be considered in lieu of formal education.

Skills

Data Lake, Data Pipelines, Data Platforms, Distributed Systems, Iceberg, Java, Python, SQL