Staff AI Platform Engineer, Corporate AI Systems
New
Remote - United StatesFull-TimeStaff
Salary169000 - 250000 USD per year
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Job Details
- Experience
- 7+ years
- Required Skills
- OAuthSalesforceJiraDevOpsConfluenceNetSuiteSlackWorkdayLLM
Requirements
- 7+ years of experience in software engineering, platform engineering, infrastructure engineering, internal developer platforms, or closely related technical roles.
- Strong hands-on experience with modern LLM and agentic systems, enterprise AI platforms, API-driven model integration, retrieval patterns, and 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, APIs, workflow platforms, and event-driven architectures in complex SaaS environments.
- Ability to balance speed, reliability, usability, and governance in building a platform that enables teams.
- Strong written and verbal communication skills, with the ability to simplify complex technical tradeoffs for various stakeholders.
- Comfort creating the first version of operating models, runbooks, patterns, and the platform itself, thriving with ambiguity.
- Outcome orientation, caring about measurable business impact over just elegant architecture.
Responsibilities
- Define and own the architecture for Cribl’s internal AI platform, LLM deployments, MCP gateway design, orchestration patterns, and shared services.
- Establish the identity and access model for AI systems, including non-human identities, scoped credentials, audit logging, cost controls, and token governance.
- Build safe, reusable sandbox environments and self-service patterns for AI experimentation within a governed framework.
- Design the connective tissue between AI tooling and Cribl’s enterprise systems, defining secure integration patterns with platforms like Salesforce, NetSuite, Workday, Jira, Confluence, Slack, Google Drive, and Glean.
- Partner 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.
- Stand up platform capabilities for AI-accelerated development, including AI coding infrastructure, guardrails, DevOps pipeline integration, and secure workflows.
- Define and track metrics for shared AI platform availability, reliability, usage, adoption, guardrail effectiveness, cost efficiency, and time to enable new use cases.
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