Senior Legal AI Platform Engineer - Cribl

Join the company that’s building the telemetry infrastructure for the AI era. At Cribl, we partner with IT and Security teams at many of the world’s biggest enterprises, including half of the Fortune 100, to bridge the gap between AI ambition and infrastructure reality. As the AI Platform for Telemetry, we give customers the choice, control, and flexibility to manage and analyze telemetry for both humans and agents, so they can build what’s next.

We’re one of the fastest‑growing private companies and a leading player in a massive, fast‑moving market. With a global workforce, we’re remote‑first and grounded in a simple idea: software is a people business. Cribl is the place where curious, collaborative people can do their best work, grow fast, and bring their full selves to the herd.

Why You’ll Love This Role

The Senior Legal AI Platform Engineer is the builder and architect inside that model—turning requirements, contract logic, risk tolerances, and service design into durable workflows, integrations, automations, agents, and technical controls. This is the administration of Legal-specific platform delivery and partners with LITS AI (Legal, IT, Security) platform engineering team and Enterprise Applications on shared infrastructure and standards. The core need is focused, high-context technical ownership: someone who can translate legal and commercial requirements across CLM, CRM, clickwrap, intake, approvals, evidence, reporting, and AI-enabled workflows into observable, governed production systems.

You’ll own the technical implementation and operation of that work while preserving the decision rights of the lawyers, privacy professionals, security partners, and business owners responsible for the underlying requirements. What you leave behind is a durable Legal runtime—not a collection of one-off automations, thin integrations, or undocumented administration.

As An Active Member Of Our Team, You Will...

• Independently own Legal AI systems and components from requirements and technical design through implementation, testing, release, operation, and documentation.
• Architect and operate integrations across CLM | CRM, intake, workflow, knowledge, identity, and collaboration systems using APIs, webhooks, queues, automation platforms, and reliable data contracts.
• Turn approved legal language, routing rules, risk thresholds, and conditioned paths into maintainable technical controls while preserving required HITL review and escalation.
• Build governed AI plugins, single-job agents, and automations that consume approved source content without duplicating or forking it across tools.
• Develop evaluation and quality controls, including representative test sets, regression checks, schema validation, traceability, and evidence showing when system behavior changes.
• Instrument the stack for reliability, auditability, model and vendor cost, and operational telemetry—feeding decision-making with high-quality data.
• Apply production controls for SSO/SCIM, service identities, scoped credentials, secrets management, privileged administration, access, logging, monitoring, incident handling, and rollback.
• Maintain automated tests, CI/CD workflows, dependency controls, release evidence, runbooks, and system maps so services remain understandable and supportable beyond any single person.
• Carry systems from problem definition through production, then establish the operating owner, maintenance model, and handoff SLA appropriate to each system.
• Partner on workflow and playbook design, reliability, cost, value signals, usability, adoption, and feedback.
• Own the technical delivery required for contracting, clickwrap, denied-party screening, privacy operations, and HR contract workflows without taking ownership away from the relevant domain lead.
• Improve self-service and low-touch contracting by encoding approved language, conditions, routing, and review gates into contract-generation and review systems.
• Partner with Legal AI Analysts and Legal stakeholders as the SME counterpart to operational design.
• Support reporting, data modeling, and operational telemetry covering automation rates, cycle times, ticket reduction, roadmap progress, satisfaction, budgeting, and broader business impact.
• Keep the boundary between shared infrastructure and Legal-specific delivery explicit.
• Act as a role model and technical mentor for others in role execution, and cross-functional collaboration.
• Work effectively across a remote-first company and multiple time zones, including occasional work outside standard hours when production needs require it.

If You’ve Got It - We Want It

• Seasoned experience in software, platform, integration, or infrastructure engineering, with independent ownership of complex production systems or components.
• Hands-on ability with a general-purpose programming language such as Python, along with APIs, webhooks, structured data, automation, and systems troubleshooting.
• Experience with cloud services, event-driven architectures, queues, containers, source control, CI/CD, automated testing, release controls, and production observability.
• Practical experience with AI/LLM systems, including model APIs, retrieval or tool-use patterns, agents, evaluation, HITL controls, and the limits of generative output.
• Strong identity and security fundamentals, including OAuth, service identities, least-privilege access, SSO/SCIM, secrets management, audit logging, and secure operational practices.
• End-to-end technical ownership of CLM, CRM, workflow, service-desk, spend-management, repository, or adjacent operational platforms.
• Strong systems thinking and data judgment, including the ability to translate legal, contractual, compliance, and policy requirements into technical controls.
• Demonstrated ability to investigate ambiguous problems, prioritize across multiple initiatives, and turn partially defined operational needs into shipped, maintainable outcomes.
• Excellent communication and change-management skills across diverse cross-functional stakeholders.
• Experience with AI governance, privacy, information governance, or legal knowledge systems.
• A strong bias toward simple architecture, durable systems of record, measurable operation, clean documentation, and scalable operating patterns.
• Experience with AWS serverless services, containerized workloads, GitHub Actions, MCP or other tool-use integrations, and production AI evaluation is a strong plus.