Staff AI Platform Engineer, Corporate AI Systems

Cribl · Enterprise · CA · IT & Security

Staff AI Platform Engineer responsible for designing, deploying, and operating a governed AI platform for internal systems and workflows. This role focuses on building the foundational infrastructure, security guardrails, and reusable patterns to enable secure and scalable AI adoption across the company, integrating with various enterprise systems and ensuring compliance.

What you'd actually do

  1. Define and own the architecture for Cribl’s internal AI platform, LLM deployments, MCP gateway design, orchestration patterns, and the shared services required to run AI use cases safely at scale.
  2. Establish the identity and access model for AI systems, including distinct non-human identities, scoped credentials, audit logging, cost controls, and token governance infrastructure that supports least-privilege access.
  3. Build safe, reusable sandbox environments and self-service patterns that allow business and technical teams to experiment with AI inside a governed framework rather than through ad hoc or unapproved tooling.
  4. Design the connective tissue between AI tooling and Cribl’s enterprise systems, helping define secure patterns for integrating with platforms such as Salesforce, NetSuite, Workday, Jira, Confluence, Slack, Google Drive, Glean, and other business-critical tools.
  5. Work hand in hand with the AI Security team to ensure secrets management, MCP governance, prompt-injection defenses, AI telemetry, and compliance-ready controls are built into the platform from day one rather than bolted on later.

Skills

Required

  • Staff-level platform engineering experience (7+ years)
  • AI platform fluency (LLM and agentic systems, enterprise AI platforms, API-driven model integration, retrieval patterns)
  • Identity, security, and governance depth (OAuth, service identities, RBAC/ABAC, auditability, secrets management, secure-by-default architecture)
  • Integration architecture expertise (enterprise systems, APIs, workflow platforms, event-driven architectures)
  • Practical systems mindset (balancing speed, reliability, usability, governance)
  • Cross-functional communication
  • Builder mentality
  • Outcome orientation

Nice to have

  • Experience with Cribl's telemetry infrastructure
  • Experience with specific enterprise systems like Salesforce, NetSuite, Workday, Jira, Confluence, Slack, Google Drive, Glean

What the JD emphasized

  • foundational builder
  • design, deploy, and operate the governed AI platform
  • secure, scalable AI
  • foundational role on a newly established team
  • provide the shared infrastructure, security guardrails, and reusable patterns
  • turn AI from fragmented experimentation into a durable company capability
  • instrumental in standing up the shared AI infrastructure layer
  • provide the “paved road” for AI at Cribl
  • secure access, governed integrations, reusable workflows
  • enables teams to move faster without creating security, compliance, or operational risk
  • foundational builder of Cribl’s shared corporate AI platform
  • define and own the architecture for Cribl’s internal AI platform, LLM deployments, MCP gateway design, orchestration patterns, and the shared services required to run AI use cases safely at scale
  • establish the identity and access model for AI systems
  • build safe, reusable sandbox environments and self-service patterns
  • design the connective tissue between AI tooling and Cribl’s enterprise systems
  • work hand in hand with the AI Security team to ensure secrets management, MCP governance, prompt-injection defenses, AI telemetry, and compliance-ready controls are built into the platform from day one rather than bolted on later
  • stand up the platform capabilities needed for AI-accelerated development
  • define and track the metrics that matter most for a shared AI platform
  • Staff-level platform engineering experience
  • track record of building shared capabilities used by many teams
  • Strong hands-on experience with modern LLM and agentic systems
  • practical realities of getting AI safely into production
  • Proven experience with OAuth, service identities, RBAC / ABAC / scoped permissions, auditability, secrets management, and secure-by-default architecture patterns
  • Experience designing and operating integrations across enterprise systems
  • Ability to balance speed, reliability, usability, and governance
  • You know how to build a platform that enables teams rather than slows them down
  • Strong written and verbal communication skills
  • simplify complex technical tradeoffs for business leaders, security partners, and technical peers alike
  • comfortable creating the first version of the operating model, the runbooks, the patterns, and the platform itself
  • Ambiguity energizes you
  • Outcome orientation
  • measurable business impact

Other signals

  • building foundational AI platform
  • enabling secure, scalable AI across internal systems
  • providing shared infrastructure, security guardrails, and reusable patterns