Staff AI Platform Engineer

Role Overview

Join Cribl as a Staff AI Platform Engineer. You will design, deploy, and operate a governed AI platform enabling secure, scalable AI across Cribl’s internal systems and workflows, including shared infrastructure and high-impact AI use cases.

Key Responsibilities

• AI platform architecture & operations: Own the design and operating effectiveness of the internal AI platform.
• LLM implementations & orchestration models: Build internal LLM integrations and orchestration/gateway patterns (including a gateway MCP design).
• Identity, access & governance: Establish models for secure access (including non-human identities), token governance infrastructure, and auditability.
• Secrets & secure-by-default patterns: Implement secure access and secrets management patterns aligned with governance.
• Sandboxed enablement: Create reusable secure sandboxes and self-service frameworks for teams to experiment with AI within a governed environment.
• Enterprise integration architecture: Design integration approaches across enterprise systems, APIs, workflow platforms, and event-driven architectures.
• AI security partnership & guardrails: Partner with security to deploy AI safety/guardrails (approval flows, policy enforcement, observability, secure coding controls).
• Platform reliability & adoption: Ensure platform reliability, usability, adoption, and runbooks/patterns that improve team effectiveness.

Requirements

• Identity & security/governance depth: Proven experience with OAuth, service identities, RBAC/ABAC, scoped permissions, auditability, secrets management, and secure-by-default architecture.
• Integration architecture expertise: Experience designing/operating integrations across enterprise systems, APIs, workflow platforms, and event-driven architectures.
• Practical platform engineering mindset: Ability to balance speed, reliability, usability, and governance.
• Outcome orientation: Focus on measurable business impact and enabling teams to ship governed AI capabilities.
• Staff-level platform engineering: 7+ years in software/platform/infrastructure/internal developer platform roles with experience building shared capabilities.
• AI platform fluency: Hands-on experience with modern LLM/agentic systems, API-driven model integration, and retrieval patterns for safe production.
• Enterprise AI platform experience: AWS Bedrock, Claude Code, or similar.
• MCP & AI agent gateway technologies: Experience with MCP, gateway/mediation concepts, and tool-use architectures.
• Guardrails for AI-assisted engineering: Approval flows, policy enforcement, observability, secure coding controls.
• Workflow/orchestration tooling: Familiarity with n8n, Workato, and similar automation platforms.
• Enterprise system familiarity: Salesforce, NetSuite, Workday, Jira, Confluence, Slack, Google Drive, Glean.
• Remote-first B2B SaaS experience: Experience operating in high-growth remote-first environments.
• Strong written and verbal communication; simplify complex tradeoffs for business, security, and technical stakeholders.

Nice-to-haves / Culture

• Good collaboration mindset across Security, IT, GTM Ops, Finance, People, and Support.
• A sense of humor (bad jokes encouraged).