Why This Job is Featured on The SaaS Jobs
This Legal AI Engineer contract sits at a practical intersection that is becoming increasingly important in SaaS: internal teams moving from individual LLM experiments to shared, governed systems. The remit is not productizing AI for customers, but operationalizing AI inside a SaaS business function where risk, auditability, and repeatability matter. That makes the work a strong example of how applied AI is being adopted beyond engineering, with legal operations acting as an internal platform team.
For a SaaS career, the distinctive value is end to end ownership of an AI enablement layer: stitching together tools, building a structured knowledge repository, and turning high volume workflows into measurable processes. The emphasis on documentation, runbooks, and handoff mirrors mature SaaS operating practices, where systems must survive beyond the initial builder and be iterated on by internal stakeholders.
This role tends to suit practitioners who like translating ambiguous business needs into working integrations and who are comfortable balancing automation with governance. It aligns with contractors or senior individual contributors who prefer project based delivery, enjoy collaborating with legal and operations partners, and want their AI work judged on reliability and adoption rather than prototypes.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
About the Role:
We're looking for a Legal AI Engineer for a fixed engagement to buildfoundational legal team AI infrastructure. You'll design and stand up these systems, document them thoroughly, and hand them off to the team in a state where they can be maintained and iterated on internally.
You'll provide services to a legal team that is already building its own AI tooling and infrastructure through Claude. Your engagement will be with the Senior Manager of Legal Contracting Operations & AI Systems. Your project will be to add the technical depth that turns promising experiments into robust, shared systems, and to deliver a coherent stack of tools and workflows.
During this engagement, you'll design workflows that are fast, measurable, and safe based on specifications from legal, legal ops, and cross-functional stakeholders. You'll leave behind documented systems and run books so the team can maintain and evolve what you've built. This engagement is equal parts legal infrastructure + applied AI, ideal for a hybrid legal/technical practitioner who can translate legal work into scalable systems and then make those systems real.
Here’s what you’ll do day-to-day during this engagement:
- Connect the AI Stack
- Take the skills, workflows, and dashboards that individual team members have already built in Claude and package them into shared, maintainable assets the whole team can use.
- Wire together current AI tooling — build MCP integrations so data flows where it's needed without manual handoffs; engage with Gusto's AIT team on connections where native integrations don't exist.
- Further operationalize AI tools and integrations as agreed upon.
- Build a Legal Repository
- Stand up a structured, queryable knowledge layer fed by the team's existing research, positions, precedents, and playbooks — one that can be searched by any team member, can be invoked by automated workflows, and continuously ingests new information as the team works in the tool.
- Implement AI Workflows
- Working from priorities set by legal ops and attorneys, implement AI-assisted workflows for the team's highest-volume use cases — likely including contract first-pass review, matter intake and routing, litigation operations to enable intake, routing and response drafting for subpoenas, demand letters and notices and prompt libraries for recurring legal tasks.
- Document and Hand Off
- Deliver a maintenance runbook for every system built and train designated team members on administration, so nothing requires the contractor to maintain it after the engagement ends. Establish adoption baselines so the team can measure progress and know what to iterate on next.
Here’s what we're looking for:
- Hands-on experience building and shipping AI-assisted workflows — not evaluating tools or writing specs, but actually standing systems up. Be ready to walk through something you built from prompt to production.
- Direct, working proficiency with Claude or comparable LLMs: prompt engineering, agentic workflows, and iterating on outputs until they're reliable. This is the core technical skill for everything we're building.
- Integration experience — you've wired tools together using APIs, MCPs, or similar patterns, and you know what breaks and why. Coordinating with engineering or IT teams to scope and execute those connections is second nature to you.
- Comfort operating in ambiguity. You can take a business problem and context from attorneys or legal ops — not a technical spec — and turn it into a working system. You know how to ask the right questions, fill in gaps with judgment, and move forward.
- Comfort working within internal technology governance processes. You know how to communicate with an IT or AI team on tool approvals, data access, and security reviews — and you don't try to go around them. You build within the guardrails, and you help the team understand why those guardrails matter.
- Bonus: Experience working alongside attorneys: you understand contracting workflows, matter intake, research cycles, and how legal teams make decisions.
Compensation will be $150/hr, approximately 20 hours per week, commensurate with experience and expertise. This is a 3-month engagement.
