Lead Product Manager (SaaS & AI Agent)

About the Role

This is CWILL's first US-based platform PM, and it's a P0 hire that reports directly to the CEO.

Your job is not to build the data infrastructure — our platform engineering team handles that. Your job is to turn what they've built into something US merchants actually want to use and pay for. You'll define the cross-product customer journeys, design the AI Agent use cases, co-own the GTM strategy, and spend real time with merchants figuring out which platform capabilities are worth building next.

The one-line mission: partner with CWILL's Platform Engineering team to turn our shared data, AI, and infrastructure capabilities into an integrated customer platform for global ecommerce brands.

What You'll Do

• Customer Discovery
• Go deep with US mid-market Shopify merchants — understand why they run multiple tools in parallel, which workflows are still manual, and where they'd actually trust an AI Agent to act autonomously
• Spend time with Sales and CS, not just in internal meetings; validate use cases with real lighthouse customers before building
• Cross-Product Platform Design
• Design end-to-end customer journeys that span multiple CWILL products — for example: shipment delay → support escalation → compensation → review recovery, or VIP churn risk → marketing suppression → human outreach
• Own the unified platform experience: information architecture, navigation, cross-product workflow design
• Define what the AI Agent should do, what it should surface for human review, and how merchants understand what happened and why
• AI Agent Product
• Define Agent tasks, tool-calling scope, user permission and approval flows, error handling, and human escalation logic
• Design the merchant-facing onboarding experience for AI features; set the bar for what "the Agent completed the task successfully" actually means
• Communicate clearly to the engineering team: we're not building another chat interface — we're building an Agent that completes a measurable merchant task
• GTM & Commercialization
• Own platform packaging and pricing decisions: what's included in base plans, what belongs in higher tiers, and how to sell a suite instead of individual apps
• Define the Lighthouse Customer program and expansion ARR playbook
• Partner with Sales and CS to drive cross-product attach rate
• Engineering Partnership
• Translate fuzzy merchant problems into crisp platform capability requirements the engineering team can build against
• Co-own prioritization decisions — not by escalating, but by building shared judgment on what's a real customer problem vs. a nice-to-have
• Reduce information loss across time zones through clear written specs and documented decisions

Requirements

What We're Looking For

• 5–8 years of product experience, with 2+ years building AI or LLM-powered products from 0 to 1
• Background in B2B SaaS — ideally multi-product customer platforms (Customer 360, workflow automation, unified inbox, CRM, cross-product journeys, or AI copilot/assistant features)
• Proven ability to do real customer discovery: talking to merchants, running pilots, and turning ambiguous feedback into a clear roadmap
• Fluent in AI application product design — agent task definition, tool calling, human-in-the-loop flows, trust and explainability — without needing to build the underlying model infrastructure
• Comfortable working across time zones with a distributed engineering team; able to reduce coordination overhead through strong written communication
• Heavy user of AI tools (GPT, Claude, Gemini, etc.) — not as a technical requirement, but as a working style
• Nice to Have
• Experience in the Shopify / DTC ecommerce ecosystem (Klaviyo, Yotpo, Aftership, Loop, Narvar, or similar)
• Seller perspective — has run an online store or worked directly with ecommerce merchants
• MarTech or post-purchase SaaS background (marketing, support, reviews, retention)
• End-to-end B2B AI Agent product launch experience
• Who You Are
• Startup mindset — you're comfortable making decisions with incomplete information and iterating fast
• You think in customer outcomes, not feature lists
• You'd rather spend a day with a merchant than a week writing specs no one's validated
• Big company or startup background doesn't matter — ability and judgment do