Staff AI Platform Engineer, Corporate AI Systems

Cribl

4d•Remote

About The Position

Cribl does differently. What does that mean? It means we are a serious company that doesn’t take itself too seriously; and we’re looking for people who love to get stuff done, and laugh a bit along the way. We’re growing rapidly - looking for collaborative, curious, and motivated team members who are passionate about putting customers first. As a remote-first company we believe in empowering our employees to do their best work, wherever they are. As the data engine for IT and Security many of the biggest names in the most demanding industries trust Cribl to solve their most pressing data needs. Ready to do the best work of your career? Join the herd and unlock your opportunity. We are seeking a talented and experienced Staff AI Platform Engineer to help build Cribl’s new Corporate AI Systems team. In this pivotal role, you will design, deploy, and operate the governed AI platform that enables secure, scalable AI across Cribl’s internal systems and workflows. This is a foundational role on a newly established team created to provide the shared infrastructure, security guardrails, and reusable patterns needed to turn AI from fragmented experimentation into a durable company capability. You will be instrumental in standing up the shared AI infrastructure layer that powers high-impact use cases across GTM, Prod-Eng and G&A functions. The team’s mandate is to provide the “paved road” for AI at Cribl: secure access, governed integrations, reusable workflows, and a platform that enables teams to move faster without creating security, compliance, or operational risk. This role will report to the Sr. Director, Enterprise Applications and will partner closely with stakeholders across Enterprise Applications Engineering, Security, IT, and the business teams adopting AI to build the core platform that every subsequent AI initiative runs on.

Requirements

Staff-level platform engineering experience: 7+ years of experience in software engineering, platform engineering, infrastructure engineering, internal developer platforms, or closely related technical roles, with a track record of building shared capabilities used by many teams.
AI platform fluency: Strong hands-on experience with modern LLM and agentic systems, including enterprise AI platforms, API-driven model integration, retrieval patterns, and the practical realities of getting AI safely into production.
Identity, security, and governance depth: Proven experience with OAuth, service identities, RBAC / ABAC / scoped permissions, auditability, secrets management, and secure-by-default architecture patterns.
Integration architecture expertise: Experience designing and operating integrations across enterprise systems, APIs, workflow platforms, and event-driven architectures in complex SaaS environments.
Practical systems mindset: Ability to balance speed, reliability, usability, and governance. You know how to build a platform that enables teams rather than slows them down.
Cross-functional communication: Strong written and verbal communication skills, with the ability to simplify complex technical tradeoffs for business leaders, security partners, and technical peers alike.
Builder mentality: You are comfortable creating the first version of the operating model, the runbooks, the patterns, and the platform itself. Ambiguity energizes you.
Outcome orientation: You care about measurable business impact, not just elegant architecture. You understand that the platform only matters if it helps Cribl ship useful, governed AI capability

Nice To Haves

Experience with AWS Bedrock, Claude Code, or similar enterprise AI platforms.
Experience with MCP, skills, gateway technologies, API mediation, tool-use architectures for AI agents.
Familiarity with orchestration and workflow platforms such as n8n, Workato, and adjacent automation frameworks.
Experience deploying guardrails for AI-assisted engineering, including approval flows, policy enforcement, observability, and secure coding controls.
Familiarity with enterprise systems such as Salesforce, NetSuite, Workday, Jira, Confluence, Slack, Google Drive, and Glean.
Experience operating in a high-growth, remote-first B2B SaaS environment.
Comfort partnering closely with Security, IT, GTM Ops, Finance, People, and Support stakeholders.
Good jokes, or maybe better, bad jokes.
A love for goats.

Responsibilities

AI Platform Architecture & Operations: 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.
Secure Access, Identity & Token Governance: 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.
Sandboxed Enablement & the Paved Road: 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.
Enterprise Integration Architecture: 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.
AI Security Partnership: 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.
Engineering Enablement: Stand up the platform capabilities needed for AI-accelerated development, including AI coding infrastructure and guardrails, DevOps pipeline integration, and secure workflows that help builders move faster without compromising quality or security.
Platform Reliability, Adoption & Operating Effectiveness: Define and track the metrics that matter most for a shared AI platform, including platform availability, reliability, usage, adoption, guardrail effectiveness, cost efficiency, and time to enable new use cases. Partner with adjacent stakeholders to support measurement of the business impact as the platform scales across the company.