About H&L

H&L is a leading hospitality technology platform serving the food & beverage and hospitality sectors across Australia and New Zealand. With more than 30 years of industry experience, H&L delivers an integrated platform spanning commerce, payments, operations, inventory, workforce management and reporting that helps hotels, pubs, restaurants, bars, clubs, cafes, liquor retailers and event venues operate efficiently and profitably.

Our platform aggregates transactions across every sale point (terminal, mobile, QR, online ordering, and increasingly voice and agentic channels), connects front-of-house commerce with back-of-house operations, advanced reporting, and integrates with more than 150 hospitality technology partners. H&L’s deep sector expertise enables operators to focus on delivering exceptional guest experiences while we focus on the platform underneath that makes it possible.

The Opportunity

H&L is moving its platform to the cloud and building AI into every layer of the venue: guest ordering, reservations and voice, the floor, the back of house, payments and intelligence. We work at startup pace across several products at once, and we need product people who turn a customer problem into a shipped, measured result in weeks.

The AI Product Manager works inside the product and engineering team and reports to the Head of Product Engineering & AI. You will spend real time in venues, prototype with AI tools before engineers commit, write the evals that decide when an AI feature is ready, and take each release to market with launch content you largely automate. Success is measured by venues live, adoption, and revenue from what you ship.

Key Responsibilities

1. Customer Discovery & Definition

• Spend time in venues every week with operators, chefs, floor staff and head office teams to see how the work is actually done

• Turn what you learn into one-page specs with the problem, a measurable outcome, and the value to the venue in dollars

• Prioritise hard with the Head of Product Engineering & AI and drop work that will not move adoption or revenue

2. AI-Native Product Building

• Prototype working solutions with AI coding and design tools (Claude Code, Cursor, Lovable or equivalents) before engineering time is committed

• Write the evals, test sets and acceptance criteria that show when an AI feature, such as voice booking, demand forecasting or anomaly detection, is ready to release

• Work with engineers in the pod through build, test and release, and own the detail: edge cases, data quality and the experience on every device

• Design for an API and MCP-first platform, so partners and agents can act on H&L data and forecasts

3. Product Operations and the Build Rhythm

• Own the delivery system end to end: the pod and sprint rituals, showcases, release notes, and the AI development rituals such as eval reviews and agent change logs, so the Head of Operations no longer runs them

• Configure and own Jira or the tool we choose: the hierarchy from roadmap to epics to stories, workflows, dashboards, and one roadmap that leadership and sales read as the source of truth

• Run the feedback loop every week: support tickets, pilot feedback and sales requests into the backlog, with what changed reported back to customers

• Automate the grunt work with agents: status updates, release notes, meeting summaries and backlog hygiene

4. Launch, Product Marketing & Early Wins