Principal GenAI Data Engineer

Zscaler accelerates digital transformation to ensure our customers can be more agile, efficient, resilient, and secure. As an AI-forward enterprise, we are constantly pushing the envelope, leveraging the world’s largest security data lake to power our cloud-native Zero Trust Exchange platform. This innovation protects our customers from cyberattacks and data loss by securely connecting users, devices, and applications in any location. Here, impact in your role matters more than title and trust is built on results. We seek innovators who actively use AI to amplify their impact and who thrive in an environment where we leverage intelligent systems to stay ahead of evolving threats. We believe in transparency and value constructive, honest debate—focused on getting to the best ideas, faster. We build high-performing teams that can make an impact quickly and with high quality through customer obsession, collaboration, ownership, and accountability.

This is a Hybrid role based in San Jose, CA or Bellevue, WA (3 days in office), reporting to the Senior Manager, Enterprise AI Data Platform in the IT Data Strategy department.

About the role

We are seeking an experienced technical leader to drive the design and implementation of enterprise-grade Generative AI data ingestion, knowledge preparation, and platform architectures that enable scalable, production-ready GenAI applications. This role focuses on architecting robust pipelines and platforms for ingesting, processing, governing, and serving structured and unstructured enterprise data for AI/LLM workloads. The ideal candidate combines deep expertise in enterprise data architecture, unstructured data pipelines, GenAI platform engineering, and strong software engineering skills in Python.

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

Qualifications

Minimum Qualifications

• Foundational understanding of AI/ML technologies and experience leveraging, securing, or positioning AI-driven solutions to optimize outcomes within your functional domain
• 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)

Preferred Qualifications

• Advanced experience architecting real-time distributed vector search infrastructure and multi-modal knowledge graph pipelines for enterprise-grade Retrieval-Augmented Generation (RAG) applications
• 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

Skills

• Thrive in ambiguity and build meaningful solutions in dynamic environments
• Act like an owner with a bias for action and integrity
• Problem-solver with a focus on delivering impactful solutions
• High-trust collaborator who values team success and constructive feedback
• Growth mindset with a passion for continuous learning and development

Pay

Base Pay Range: $182,000—$260,000 USD (excludes commission/bonus/equity, if applicable, and benefits)

Benefits

• Various health plans
• Time off plans for vacation and sick time
• Parental leave options
• Retirement options
• Education reimbursement
• In-office perks, and more