Senior Legal AI Platform Engineer

Responsibilities

• 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.
• Translate approved legal language, routing rules, risk thresholds, and conditioned paths into maintainable technical controls while preserving human review and escalation.
• Build governed AI plugins, single-job agents, and automations using approved source content.
• Develop evaluation and quality controls, including representative test sets, regression checks, schema validation, traceability, and change evidence.
• Instrument systems for reliability, auditability, model and vendor cost, and operational telemetry.
• 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.
• Carry systems from problem definition through production and establish ownership, maintenance models, and handoff SLAs.
• Support contracting, clickwrap, denied-party screening, privacy operations, HR contract workflows, reporting, data modeling, and operational telemetry.
• Partner with Legal AI Analysts and stakeholders to resolve system behavior, data quality, usability, and adoption issues.
• Mentor others and collaborate across a remote-first, multi-time-zone organization.

Requirements

• Seasoned experience in software, platform, integration, or infrastructure engineering, with independent ownership of complex production systems or components.
• Proficiency with a general-purpose programming language such as Python, 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, model APIs, retrieval or tool-use patterns, agents, evaluation, human-in-the-loop controls, and generative AI limitations.
• Strong identity and security fundamentals, including OAuth, service identities, least-privilege access, SSO/SCIM, secrets management, audit logging, and secure operations.
• End-to-end ownership of CLM, CRM, workflow, service-desk, spend-management, repository, or adjacent operational platforms.
• Ability to translate legal, contractual, compliance, and policy requirements into workflows, fields, conditions, repositories, technical controls, and auditable evidence.
• Strong problem-solving, prioritization, communication, and change-management skills.
• Experience with AI governance, privacy, information governance, or legal knowledge systems.
• Comfort with reporting, metrics, dashboards, and BI-adjacent outputs.
• Bias toward simple architecture, durable systems of record, measurable operations, clean documentation, and scalable operating patterns.

Nice to Have

• Experience with AWS serverless services, containerized workloads, GitHub Actions, MCP or other tool-use integrations, and production AI evaluation.