Principal GenAI Data Engineer

Zscaler•Santa Clara, CA

1d•$185,500 - $265,000•Hybrid

About The Position

Zscaler is seeking a Principal GenAI Data Engineer to lead the design and implementation of enterprise-grade Generative AI data ingestion, knowledge preparation, and platform architectures for scalable, production-ready GenAI applications. The role focuses on architecting platforms and pipelines to ingest, process, govern, and serve structured and unstructured enterprise data for AI and LLM workloads. The ideal candidate brings expertise in enterprise data architecture, unstructured data pipelines, GenAI platform engineering, and Python software engineering. This is a hybrid role based in Santa Clara, California, or Bellevue, Washington, with three days in the office. The position reports to the Senior Manager, Enterprise AI Data Platform, in the Information Technology Data Strategy department. Zscaler describes its culture as centered on ownership and collaboration, trust through outcomes and impact, and a challenge culture with ongoing feedback. The company offers comprehensive benefits and is committed to an inclusive workplace and equal employment opportunity.

Requirements

Foundational understanding of AI/ML technologies and experience leveraging, securing, or positioning AI-driven solutions to optimize outcomes within a functional domain.
Expert-level Python programming and software engineering capabilities.
Experience building distributed and scalable data pipelines for AI workloads.
Strong understanding of unstructured data extraction and processing pipelines.
Experience with vector databases, graph databases, and metadata or knowledge storage systems.
Hands-on experience with clustering, entity recognition algorithms, and modern retrieval strategies, including RAG, search, and agentic AI workflows.

Nice To Haves

Advanced experience architecting real-time distributed vector search infrastructure and multimodal knowledge graph pipelines for enterprise-grade RAG applications.
Experience with LLMOps or GenAIOps frameworks such as LangSmith, evaluation frameworks such as Arize Phoenix, Weights & Biases, or MLflow.
Familiarity with agent frameworks such as LangGraph, CrewAI, or Google ADK.

Responsibilities

Architect enterprise-scale GenAI data platforms for ingestion, transformation, enrichment, and serving of structured and unstructured data.
Design scalable pipelines for enterprise knowledge ingestion from documents, SaaS platforms, knowledge bases, collaboration tools, and databases.
Define architecture for metadata extraction, chunking, enrichment, embeddings generation, and knowledge preparation workflows.
Design AI-ready data models and storage strategies for vector, graph, and hybrid knowledge systems.
Architect scalable unstructured data processing pipelines for text, images, PDFs, tables, and multimodal content.