Principal Product Manager, Bryte AI Platform

Bryte AI Platform Product Manager

Own product strategy and execution for Bryte's AI platform capabilities, including conversational agents, intent classification, tool orchestration, SDKs, and shared AI services. Drive the architecture and roadmap for multi-agent systems, including how agents route intents, select tools, handle failures, and degrade gracefully. Think deeply about the customer experience: how AI features surface in-app, how they build trust, how they drive adoption organically. This isn't just about shipping capabilities; it's about designing experiences that make customers want to use AI more. Own the narrative. Translate complex AI systems into clear, compelling stories for customers, GTM teams, and leadership. Articulate applied use cases that connect technology to real customer outcomes. Drive GTM readiness for your releases by partnering with enablement, sales, and customer success to ensure features land well. Understand rollout strategy, customer segmentation, and adoption mechanics. Define and own AI quality metrics (helpfulness rates, failure mode taxonomy, out-of-domain deflection rates) and run the feedback loops that drive continuous improvement. Translate complex customer and business problems into clear requirements, priorities, and tradeoffs. Write PRDs and decision docs that don't need follow-ups. Work directly in the codebase: read agent code, debug production issues from logs and telemetry, prototype integrations, and submit changes when appropriate. Drive delivery across multiple engineering teams and dependencies, even when no one reports to you. Partner with engineering and AI teams to make pragmatic tradeoffs on scope, quality, speed, and risk, with enough technical depth to challenge and be challenged. Surface risks early, propose solutions, and follow through. Communicate effectively across levels: engineers, partners, and senior leaders.

7-8+ years of Product Management experience in enterprise SaaS. Shipped AI-powered products with direct involvement in the underlying architecture, not just "we added AI to the product." Strong understanding of LLM-based agent systems: multi-agent orchestration, intent classification, tool routing, prompt engineering, and failure handling. Comfortable reading code, navigating a codebase, and debugging alongside engineers. You've submitted PRs, reviewed code, or prototyped solutions yourself. Experience defining and tracking AI quality metrics: accuracy, helpfulness, failure modes, regression detection. Exceptional communicator and storyteller. You can explain a complex AI system to an engineer, pitch its value to a customer, and present a strategy to a VP, adjusting depth and framing for each audience. Product-led growth mindset. You've thought about how features drive adoption, engagement, and expansion, not just how they work technically. You understand the intersection of technology and usage. Proven ability to lead through influence and operate across teams without formal authority. Complete PM toolkit: strong across process, technical depth, domain understanding, influence, and strategy. Not just one dimension. Quick to grasp new domains, frameworks, and system architectures. Strong ownership mindset. Takes action, closes loops, doesn't wait to be told. Even-keeled, reliable, and calm under pressure. Comfortable working across highly technical and non-technical audiences. Platform thinking: you understand that your "customers" include internal product teams consuming your SDKs and services, and you balance their needs against end-user outcomes. Customer empathy at the experience level. You think about what it feels like to use the product, not just what it does. You can inspire confidence in AI capabilities through thoughtful UX and clear value demonstration. Hands-on experience with agent frameworks such as Claude API/tool-use, Google ADK, LangGraph, or similar. Background in HCM, payroll, or regulated enterprise environments where AI decisions have compliance implications. Experience with AI trust, safety, or guardrails (content filtering, PII handling, model governance). Familiarity with AI evaluation tooling and observability platforms. Platform or developer tools product management experience. GTM experience: you've partnered on launch strategy, customer enablement, or adoption programs for technical products.