Principal Engineer - Enterprise Content and AI Data Platform

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

NVIDIA is seeking a Principal Engineer to lead the development of three interconnected platforms: sensitive information protection and DLP, enterprise data governance, and an enterprise AI knowledge platform. This role involves owning roadmaps, driving integration of enterprise content for AI agents, and ensuring access control and export control enforcement. The engineer will also mentor other engineers and potentially grow into a management role. Experience with AI/LLM data pipelines, vector stores, or RAG architectures is highly desirable.

What you'd actually do

  1. Own the roadmap for sensitive-information detection and remediation — partnering with Finance, Legal, and Security to define classification models, remediation workflows, and reporting that leadership can trust.
  2. Drive production rollout and self-service onboarding for the enterprise data access platform, spanning connectors across email, messaging, document stores, and search (Outlook, Teams, Slack, Confluence, OneDrive, SharePoint, Google Drive, Glean).
  3. Drive integration of enterprise content sources — document stores, wikis, and cloud drives — into the AI knowledge platform, ensuring content is accurate, fresh, and access-controlled for agent consumption.
  4. Serve as the technical lead across all three workstreams, aligning stakeholders in Security, Finance, Legal, and AI platform teams and driving clarity on ownership and priorities.
  5. Mentor engineers and foster a culture of documentation, runbooks, and operational rigor; demonstrate readiness to grow into an engineering management role.

Skills

Required

  • Bachelor's or Master's Degree in Computer Science, Computer Engineering, or a related field (or equivalent experience).
  • 15+ years of experience building and operating large-scale enterprise platforms, with a track record of growing technical scope and influence beyond individual execution.
  • Strong foundation in backend systems, distributed systems, and high-performance computing — experience with large-scale data processing, indexing pipelines, and systems designed for reliability and scale.
  • Experience building or integrating secure API platforms, data connectors, or enterprise SaaS integrations at scale (e.g., Confluence, SharePoint, Google Drive, Slack, Teams, or similar).
  • Proven ability to drive cross-functional alignment across Security, Legal, Finance, and platform engineering teams.
  • Excellent written and verbal communication skills; ability to translate complex technical tradeoffs into executive-level clarity.
  • Demonstrated interest in growing into engineering leadership — experience mentoring peers, leading projects, or taking on informal leadership responsibilities is a strong signal.
  • Comfortable holding ambiguity and driving decisions in a fast-paced, high-stakes environment.

Nice to have

  • Background in enterprise security, data governance, or compliance platforms — familiarity with classification, remediation workflows, access control models, and audit requirements is a plus.
  • Experience with AI/LLM data pipelines, vector stores, or RAG architectures — particularly in connecting enterprise content sources to AI platforms with strict access controls.
  • Hands-on experience with Databricks or similar platforms for audit logging, data governance, and RBAC. Familiarity with Glean or similar enterprise search/DLP products and their integration patterns.
  • Experience with vendor evaluation and build-vs-buy decisions for enterprise security or content platforms.
  • Ability to leverage AI and agentic automation to drive operational efficiency and reduce engineering toil.
  • Track record of leading platform migrations, tenant consolidations, or g

What the JD emphasized

  • production readiness
  • access control fidelity
  • export-control enforcement
  • production readiness gates
  • strict access controls

Other signals

  • enterprise AI knowledge platform
  • AI agents at scale
  • content for reliable consumption by AI agents
  • connecting enterprise content sources to AI platforms with strict access controls