[Hiring] Senior Legal AI Platform Engineer @Cribl

Role Description

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.

The roadmap includes:

• AI plugins and single-job agents
• Privacy software implementation
• CRM | CLM continuity
• Denied-party integrations
• Product and partner clickwrap
• CLM infrastructure
• Data & security pipelines | lakes | observability
• HR contract workflows
• Content and routing distribution
• System documentation alignment
• Broader AI and intelligence enablement

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.

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—using service pain points, process observations, and user feedback to resolve issues in system behavior, data quality, usability, and adoption.
• 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, using common platform capabilities where they fit and building Legal-owned solutions where distinct requirements or elevated risk justify them.
• 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.

Qualifications

• 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—from architecture through maintenance, not only configuration at the margins.
• Strong systems thinking and data judgment, including the ability to translate legal, contractual, compliance, and policy requirements into workflows, fields, conditions, repositories, technical controls, and auditable evidence.
• Good judgment in selecting the methods and techniques used to build solutions.
• Demonstrated ability to investigate ambiguous problems, prioritize across multiple initiatives, and turn partially defined operational needs into shipped, maintainable outcomes with limited day-to-day direction.
• Excellent communication and change-management skills across engineers, lawyers, analysts, security partners, finance stakeholders, People teams, and business-system owners.
• The ability to advocate firmly for core convictions while remaining adaptable and keeping scope, ownership, and tradeoffs clear.
• Experience with AI governance, privacy, information governance, or legal knowledge systems, particularly where the work requires balancing enablement with control.
• Comfort working with reporting, metrics, dashboards, and BI-adjacent outputs that help Legal understand operational performance and communicate value.
• A strong bias toward simple architecture, durable systems of record, measurable operation, clean documentation, and scalable operating patterns—not heroics, one-off fixes, or undocumented admin work.
• Experience with AWS serverless services, containerized workloads, GitHub Actions, MCP or other tool-use integrations, and production AI evaluation is a strong plus.