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The Legal Engineer – Litigation is the builder on that team. The role turns litigation use cases into reliable, governed, and reusable AI-enabled workflows—designing prompts, agents, and retrieval pipelines; building evaluation sets and quality controls; and standing up production-ready solutions that hold up to the demands of legal work.Â
This is an opportunity for a builder who understands how litigation actually works and wants to engineer the AI-enabled version of it. The ideal candidate pairs hands-on fluency with generative AI platforms and workflow tools with enough legal understanding to know what “good” looks like, where AI is appropriate, and where human oversight is non-negotiable.
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Responsibilities
Build and engineer AI-enabled litigation workflows
Design, build, test, and deploy AI workflows, agents, and skills across the firm’s approved AI platforms, supporting litigation use cases such as document review, discovery responses, deposition preparation, chronology building, brief and pleading drafting, deficiency letters, and summarization.
Develop and maintain prompt libraries, retrieval pipelines, document templates, decision trees, and reusable workflow components with clear, attorney-ready documentation.
Translate practice requirements—gathered directly or in partnership with the AI Innovation Attorneys—into technical specifications, workflow logic, and structured solutions.
Where appropriate, use low-code tools, automations, and integrations to extend firm-approved platforms and connect AI workflows to firm systems.
Test, evaluate, and harden solutions
Build and run evaluation frameworks to measure workflow quality, accuracy, completeness, and reliability against representative matters, exemplar work product, and lawyer-defined quality standards.
Embed human-review checkpoints, guardrails, and audit mechanisms aligned with firm governance, confidentiality, and professional-responsibility requirements.
Document testing protocols, outcomes, and lessons learned; identify, log, and escalate risks and failure modes as appropriate.
Deploy, maintain, and scale
Support the rollout of workflows into daily practice, partnering with the AI Innovation Attorneys and adoption resources on launch training and early support.
Own ongoing maintenance of production workflows, updating prompts, retrieval sources, and guardrails in response to model changes, platform updates, user feedback, and evolving practice needs.
Develop and scale best-practice standards, use-case libraries, and playbooks that help lawyers responsibly and independently leverage AI tools while maintaining quality and risk controls.
Track adoption, usage, quality, and impact metrics for deployed workflows, and recommend refinements or expansion opportunities.
Collaborate across the firm
Partner closely with the AI Innovation Attorneys, applied AI and legal-engineering colleagues, Knowledge Management, Information Services, data and security teams, Professional Development, and vendors to deliver reliable, supportable solutions.
Serve as a technical translator among legal, engineering, and business audiences, turning ambiguous needs into practical, production-ready solutions.
Stay current on generative AI, agentic workflows, litigation technology, and the vendor market, translating developments into concrete build recommendations.
Qualifications
J.D. preferred but not required; candidates with equivalent substantive legal, litigation-support, knowledge-management, or legal-technology experience are strongly encouraged to apply.
3–5 years of experience in legal practice, legal technology, knowledge management, legal operations, practice innovation, or a comparable professional-services environment, with demonstrated hands-on work building AI, automation, or workflow solutions.
Hands-on AI build experience—prompt engineering, AI workflow or agent development, and configuring legal AI platforms (e.g., Microsoft Copilot, Harvey, CoCounsel, Claude, or similar), including familiarity with retrieval-augmented generation (RAG) concepts and evaluation of model outputs.
Working understanding of litigation workflows and attorney work-product standards, with the judgment to recognize where AI is—and is not—appropriate.
Quality and risk discipline—an instinct to build in testing, human-review checkpoints, and verification steps for legal output.
Comfort iterating in ambiguity—scoping a use case, building a prototype, gathering feedback, and refining toward a production-ready workflow.
Strong communication and collaboration skills, with the ability to build trusted relationships with attorneys at all levels and teach AI and workflow concepts to non-technical audiences in practical terms.
Light coding fluency (e.g., Python, SQL, or scripting) and experience with workflow builders, low-code platforms, or integrations are a plus but not required.
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