About the Role

Our team needs a Senior Program Manager to own end-to-end delivery of GenAI-powered features — from initial concept through architecture, build, launch, and scale. This is a hands-on, technically proficient PGM role. You will lead the roadmap and collaborate directly with engineers to build system architecture. You will also be accountable for adoption and outcomes after the feature ships. You'll operate at the intersection of product strategy, applied AI architecture, and cross-functional execution.

What You'll Do

• Own the Feature Roadmap End-to-End — Define and drive the roadmap for GenAI-powered features from concept to launch, aligning scope and sequencing with business priorities. Make the call on model bets and platform direction at the feature level.
• Architect and Ship Agentic/RAG Systems — Lead and facilitate the design and delivery of the AI agents, multi-agent orchestration, and RAG pipelines underlying the feature, from prototype through production-grade deployment. Partner directly with engineering on system architecture, not just program tracking.
• Drive Adoption from Beta to Default — Own the process for go-to-market and adoption plan; run pilots, gather usage signals, iterate on feedback, and build the mechanisms that move the feature from experimental to default usage.
• Run Governance and Trade-offs Within the Feature Team — Set facilitation for prioritization and resourcing calls across workstreams (model work, UX, infra, QA). Run steering and status reviews, and resolve trade-offs across engineering, design, and product.
• Partner Deeply on Technical Execution — Collaborate closely alongside engineers on model selection, timely/eval frameworks, MLOps practices, and orchestration build. Provide credible, hands-on technical review and pushback throughout the build — this isn't a coordination-only role.
• Communicate Progress and Risk Upward — Translate build status, risks, and outcomes into clear executive narratives — status reports, launch readiness reviews, QBRs. Distinguish what needs escalation from what's routine.
• Codify Reusable Patterns — Document architecture, eval approach, and rollout playbooks so future GenAI features can reuse them instead of starting from scratch.
• Own Outcome Metrics — Define success metrics and instrumentation pre-launch; monitor post-launch performance against engagement, quality, and business-impact targets, and course-correct based on data.