Our product roadmap increasingly depends on AI doing real work inside the product: extracting structured data from documents, and running agents that take action on a user’s behalf. That only works if two things are true: the underlying extraction is accurate enough to trust, and every AI action is governed (auditable, explainable, escalated to a human when it should be, and safely isolated between customers).
This role owns that platform. You turn “the AI isn’t accurate enough yet” and “agents need guardrails” into a roadmap, a trust bar, and an interface that other product teams can build against without a standing dependency on one engineer.
Day to Day
Extraction accuracy
Own the roadmap to improve document/data extraction accuracy against defined targets, including major build-vs-improve calls on the underlying approach, in partnership with ML/engineering.
Define what "accurate enough to trust" means for each use case, and track progress against it on a live, shared metric.
Agent runtime & governance
Own product requirements for the runtime that lets AI agents take actions inside the product.
Own the governance layer: audit trail, explainability, human-in-the-loop escalation, policy enforcement, and tenant isolation. This is what makes an AI-driven action safe to ship, not just possible to build.
Define a policy-bounded interface for agent-triggered actions, so actions stay within defined guardrails without requiring engineering review per action.
Platform-as-a-service discipline
Define and maintain a self-serve interface so other product teams can build on this platform without filing a ticket to a single engineer for routine requests.
Partner with adjacent platform owners (e.g., core integrations/data) so teams building on top see one coherent surface, not competing conventions.
Push back on one-off requests that would turn the platform back into a bottleneck.
What success looks like (first 2-3 quarters)
A clear, evidence-based call made on the extraction approach, with a dated accuracy trajectory behind it rather than opinion.
Defined accuracy targets met on priority use cases, tracked on a live dashboard.
At least one AI-driven agent live in production with the escalation/governance loop actually working, not just the underlying model.
At least one other product team consuming the platform self-serve, with no standing ticket queue for routine requests.
Usage and override rates on agent-triggered actions instrumented and reviewed.
Requirements
3+ years in product management, including a stretch owning a platform, API, or infrastructure-facing product used by other internal teams, not exclusively customer-facing feature work.
Direct experience shipping ML/AI-backed product where trust and accuracy were the product, not a feature bullet. You’ve defined a precision/recall bar and held engineering to it.
Working fluency in what “governance” means for an AI system in production: audit trails, explainability, human-in-the-loop design, policy enforcement, tenant/data isolation. You don’t need to have built all of these, but you’ve shipped alongside at least one and know why each exists.
Comfortable owning a roadmap with genuine capability uncertainty and communicating that uncertainty honestly to stakeholders instead of smoothing it over.
Platform-PM instincts: default to “how do I make this self-serve” over “how do I build this feature for one team” Says no to scope creep that would make you a bottleneck.
Senior-level autonomy: given a problem area rather than a spec, you set the why, frame the what and how, and are trusted with the area’s roadmap and prioritization end to end.
Nice to have
Prior experience in a regulated domain where explainability/audit was a hard requirement rather than a nice-to-have.
Exposure to agent-action protocols (e.g., MCP) or policy-engine design for agentic systems.
Bonus points
None of these are required, but they will set you apart
CLM, legal-tech, or contracts domain experience.
Direct customer-facing experience in a Sales, Customer Success, Solutions, or pre-sales role or a track record of close partnership with those teams.
Experience shipping AI-native or AI-assisted products to production.
A background in a fast-scaling B2B SaaS environment.
Why SpotDraft?
Brilliant teammates—Work with some of the sharpest minds in legal tech.
Expand your network—Interact with top founders, investors, and industry leaders.
Real impact—Take ownership of projects and see your work in action.
Big goals, bold moves—We trust you to deliver, innovate, and push boundaries.
Our Core Values
Our business is to delight Customers
Be Transparent. Be Direct
Be Audacious
Outcomes over everything else
Elevate each other
Be Passionate. Take Ownership.
Be 1% better every day
All candidates’ personal data shared during the recruitment process will be handled with utmost confidentiality and used solely for hiring purposes, in line with applicable data protection regulations.
SpotDraft is an equal-opportunity employer. Candidates will not be discriminated against based on race, ethnicity, color, religion, caste, sex, gender identity, sexual orientation, national origin, veteran, or disability status
