Principal GenAI Data Engineer

Zscaler accelerates digital transformation to ensure customers can be more agile, efficient, resilient, and secure. They are seeking a Principal GenAI Data Engineer to drive the design and implementation of enterprise-grade Generative AI data ingestion and platform architectures, focusing on robust pipelines for processing structured and unstructured data for AI workloads.

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 diverse data sources including 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

Requirements:

• Expert-level Python programming and software engineering capabilities
• Experience building distributed/scalable data pipelines for AI workloads
• Strong understanding of unstructured data extraction and processing pipelines
• Experience with vector databases, graph databases, and metadata/knowledge storage systems
• Hands-on experience with clustering, entity recognition algorithms, and modern retrieval strategies (including RAG, search, and agentic AI workflows)
• Deep understanding of AI-ready data platform design principles and the ability to bridge platform/data engineering with GenAI/LLM application requirements
• Experience with LLMOps / GenAIOps frameworks such as LangSmith, Evaluation Framework like Arize Phoenix, Weights & Biases, or MLflow
• Familiarity with Agent Frameworks like LangGraph, CrewAI, or Google ADK