About The Role

The Legal Engineer – Transactional is the builder on that team. Where our Innovation Attorney (Transactional) partners with Business Law practice groups to diagnose workflows and decide what to solve, the Legal Engineer owns how those solutions get built, tested, and maintained. The role turns diligence, drafting, and deal-support 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 transactional practice.

This is an opportunity for a builder who understands how deal work actually happens 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 transactional understanding to know what “good” looks like, where AI is appropriate, and where human oversight is non-negotiable.

Build and engineer AI-enabled transactional workflows

Design, build, test, and deploy AI workflows, agents, and skills across the firm’s approved AI platforms, supporting transactional use cases such as diligence review, first-pass agreement review, playbook-driven redlining, clause extraction, deal-term summarization, and closing-checklist automation.
Develop and maintain prompt libraries, retrieval pipelines, clause banks, document templates, and reusable workflow components with clear, attorney-ready documentation.
Translate practice requirements—gathered directly or in partnership with the Innovation Attorney (Transactional)—into technical specifications, workflow logic, and structured solutions.
Where appropriate, use low-code tools, automations, and integrations to extend firm-approved platforms (e.g., Harvey, Legora , CoCounsel , Copilot, or contract-review 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 deals, 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 deal practice, partnering with the Innovation Attorney (Transactional) and adoption resources on launch training and early support.
Own ongoing maintenance of production workflows, updating prompts, retrieval sources, clause banks, and guardrails in response to model changes, platform updates, user feedback, and evolving deal 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 Innovation Attorney (Transactional), applied AI and legal-engineering colleagues, the Business Law Knowledge Management Lawyer, 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, transactional legal technology, and the vendor market, translating developments into concrete build recommendations.

No Job Description Loaded

J.D. preferred but not required; candidates with equivalent substantive legal, deal-support, knowledge-management, or legal-technology experience are strongly encouraged to apply.
3–5 years of experience in transactional 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., Harvey, Legora , CoCounsel , Microsoft Copilot, or contract-review platforms such as Kira or Luminance), including familiarity with retrieval-augmented generation (RAG) concepts and evaluation of model outputs.