Casepoint

Principal Product Lead, AI

Casepoint

full-time

Posted on:

Location Type: Remote

Location: Arizona • California • United States

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Salary

💰 $155,000 - $185,000 per year

Job Level

Senior

About the role

  • Define and maintain the AI product roadmap across Casepoint's full product portfolio, prioritizing AI agents, LLM-powered workflows, and shared AI capabilities in a sequence that reflects customer value and business goals.
  • Use AI-assisted development tools, LLMs, and vibe coding techniques to prototype solutions and produce working demonstrations for customers and internal stakeholders before committing to full engineering efforts.
  • Lead AI feature delivery from discovery through launch by establishing evaluation standards and acceptance criteria that Engineering and QA can build to reliably in legal and compliance contexts where accuracy and auditability are required.
  • Engage with customers directly to validate AI concepts, surface expectations around accuracy and explainability, and develop the kind of trust that supports adoption in enterprise environments with significant regulatory obligations.
  • Work with Sales and Customer Success to articulate AI capabilities in terms that resonate with customers and prospects, supporting competitive evaluations and helping teams communicate value in clear, credible terms.
  • Act as an AI product authority across the portfolio by establishing deployment standards, advising fellow product team members on AI integration, mentoring and coaching on AI product thinking and execution, and ensuring AI capabilities are built consistently and positioned coherently across Casepoint's platform.
  • Define AI-specific performance metrics, including accuracy, adoption, and business impact, and use post-launch data to drive iterative improvement across all AI-powered features.

Requirements

  • A background shipping AI or ML-powered features in enterprise B2B SaaS products, with direct experience taking AI capabilities from concept through production in environments where quality and reliability are non-negotiable.
  • Working knowledge of modern LLM patterns and tradeoffs (e.g., retrieval-augmented generation, embeddings/vector search, tool/function calling), including how cost, latency, and accuracy considerations shape product decisions.
  • Experience building and operating AI features responsibly in regulated environments, including partnering on privacy/security reviews, defining monitoring and human-in-the-loop workflows, and managing model behavior over time (drift, regressions, and changing customer expectations).
  • Hands-on experience with LLMs, AI coding assistants, and prototyping tools, with a habit of using these tools actively in your product work to validate ideas quickly and generate working demonstrations.
  • The ability to explain AI capabilities, their limitations, and how they work in accessible terms to a range of audiences, including legal and compliance professionals, technical engineers, and executive stakeholders.
  • Experience or exposure to legal technology, eDiscovery, or regulated industry software, with enough domain context to understand where AI can be appropriately trusted and where it requires additional safeguards.
  • Strong cross-functional collaboration skills, with a track record of aligning Engineering, Data Science, Sales, and Customer Delivery teams around a shared product direction.
  • Experience mentoring and coaching colleagues and cross-functional partners, raising the quality bar for AI product discovery, evaluation, and delivery through guidance, reviews, and shared standards.
  • Comfort operating in conditions of ambiguity, making reasonable decisions with incomplete information, and adjusting course as the technology, the market, and customer expectations continue to develop.
Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools
AI product roadmapLLM-powered workflowsAI-assisted development toolsvibe coding techniquesAI feature deliveryperformance metricsaccuracyadoptionbusiness impactAI capabilities
Soft Skills
cross-functional collaborationmentoringcoachingcommunicationcustomer engagementtrust buildingexplanation of AI conceptsdecision making in ambiguityiterative improvementstakeholder alignment