Product Manager, AI Enablement

Pindrop is a cybersecurity company delivering continuous identity verification across voice, video, and digital interactions. The Product Manager, AI Enablement will turn Pindrop's AI capabilities into practical internal workflows by mapping processes, building and deploying agentic solutions, measuring ROI, and driving adoption across business functions.

Responsibilities:

• Map processes before touching AI. Sit with stakeholders, document current-state workflows end to end, extract the context and tools involved, and identify precisely where AI can reduce friction - not the other way around
• Architect, build, and deploy a portfolio of internal AI tools, agentic workflows, and custom integrations (via APIs and automation frameworks) that solve discrete, measurable problems—pushing every project past prototype into a stable, production-ready state
• Audit AI capabilities already built into tools Pindrop has purchased before standing up new solutions; use what we've paid for first
• Extend Pindrop's shared AI platform and agent infrastructure to new use cases rather than creating parallel stacks
• Design and implement the operating process that moves AI concepts from prototype to production, including evaluation, human-in-the-loop review, rollout criteria, and post-launch monitoring
• Consolidate learnings across functions - what worked in Finance should be usable in People; what worked in People should be accessible to CS - and build the tooling and documentation that makes reuse real
• Make the ROI case. Define success metrics, instrument usage, track adoption, and be able to explain clearly what the company is getting for its AI investment
• Drive adoption through practical enablement - workshops, onboarding paths, office hours, and coaching - focused on giving teams the guardrails they need to use AI well, not just permissively

Requirements:

• A technical builder and product thinker: You go far beyond basic prompt engineering into true technical building—backed by a GitHub profile (or equivalent portfolio) with shipped, functional code that proves it
• Process mapper first. Before you touch a tool, you understand the workflow. You can sit with a stakeholder, draw out what actually happens today, and find the leverage point because you know you can't automate a process you haven't mapped
• Frontier-fluent and self-directed. You track what's happening at the edge of AI tooling because you're genuinely curious, not because it's required. You know the difference between AI theater and AI value, and you're allergic to the former
• A translator and teacher. You can walk into a Finance or HR meeting without owning their domain and still build credibility through preparation, curiosity, and practical results. You get energy from turning a skeptic into a confident user
• Rigorously skeptical. You stress-test outputs, understand where models break down, and hold a high bar before recommending a workflow to a real team. You know what an eval is and why it matters
• Process mapping. You must be able to document a current-state workflow end to end, extract the tools and context involved, and use that map as the foundation for any AI solution
• 3+ years in product management, product operations, AI enablement, or a closely related role at a technology company with PM-level ownership of scope, requirements, delivery, and metrics
• Direct experience scripting (e.g., Python), integrating APIs, and deploying agentic systems that solve discrete, measurable operational problems—shipped solutions, not just demos
• Hands-on fluency with frontier AI CLIs and coding tools (e.g., Claude Code, Codex, Cursor)
• Fluency in context engineering and ontology design; ability to speak to both concepts and apply them in practice
• Conceptual command of test-driven development and eval-driven development for AI systems; must understand why evals matter and how to define them, even if you're not writing them by hand
• Proven ability to define success metrics, track adoption, and make the ROI case for AI investments to a skeptical stakeholder
• A GitHub profile showing 3–4 built projects or repositories is a strong signal; candidates without one will be asked to walk through their technical architecture in detail
• Experience with meta harnesses and multi-agent orchestration frameworks
• Working knowledge of Python, API integrations (REST/GraphQL), and building custom tools using frameworks like Model Context Protocol (MCP) or similar orchestration layers
• Familiarity with RAG patterns, LLM output evaluation, hallucination mitigation, and release gating in enterprise contexts
• Exposure to AI acceptable-use policies, data-handling guardrails, or workforce governance frameworks
• Background in high-growth, AI-native, or B2B SaaS environments where technology is advancing faster than organizational absorption
• Experience building enablement content, use-case libraries, or office-hours programs from a blank page