Senior Forward Deployed Product Manager
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Job Description
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Lorikeet is redefining what customer support looks like, building AI that resolves complex, high-volume support for fintechs, healthtechs, marketplaces, and delivery services. Backed by over $50M USD from QED, Blackbird, and Square Peg, Lorikeet serves customers handling over a million support tickets a year.
About the Role
As a Senior Forward Deployed Product Manager, you’ll own strategic accounts and drive outcomes from Lorikeet’s Concierge AI system, which handles full customer interaction lifecycles across channels and systems. You’ll:
- Lead deployments for highest-value customers, aligning executives and engineers on priorities and success metrics.
- Own account growth, retention, and usage post-launch, identifying expansion opportunities.
- Investigate Concierge improvements, build and deploy fixes in production, and collaborate with Forward Deployed Engineers (FDE).
- Partner directly with customer executives to define next use cases and expansion strategies.
- Navigate procurement, security, and legal reviews for deployments.
- Coach Forward Deployed Product Managers (FDPMs) and create playbooks for scaling best practices.
- Drive product improvements based on customer evidence, ensuring solutions ship.
Example Projects
- Build a voice agent for live currency trades, handling identity checks and trade confirmations.
- Deploy Concierge as a 24/7 fintech phone line, automating card replacements and fraud cases.
- Automate identity document verification for money transfer apps, improving fraud detection.
- Invent customer retention workflows for cancellations, aligned with business rules.
- Develop AI-driven verification for locked-out customers via linked social accounts.
Qualifications
You’re a fit if you have:
- 6+ years owning high-stakes customer outcomes (account leadership, product, consulting, or founding).
- Proven track record of growing enterprise accounts through renewals and expansions.
- Startup/scale-up experience with process-building alongside execution.
- Hands-on AI product development experience and insights on what works.
- Ability to diagnose issues using API docs, conversation logs, and usage data.
- History of improving team performance through management or systems.
Bonus:
- Experience in fintech, healthcare, or regulated industries.
- Shaping product roadmaps based on customer evidence.
- Deploying AI products tied to measurable business outcomes.
Why This Opportunity Stands Out
Culture: Low ego, high trust, flexible hours, and a commitment to diversity. No tolerance for talented jerks.
Impact: Series A-stage startup with real scope to shape product and company direction.
Growth: Promotions based on readiness, not arbitrary timelines. Leadership reviews every 6 weeks to recognize performers.
AI-Native: Unrestricted access to AI tools (e.g., Claude Code) for daily work, equipping you for future roles.
Team Bonding: Twice-yearly company retreats to Hawaii for collaborative hackathons and idea-building.
Recruitment: Respectful process: informal chats + a paid 2-day work trial to assess fit.
Underrepresented candidates: Apply anyway. Diversity drives performance, and Lorikeet reflects a broad spectrum of identities.
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Lorikeet
View Company ProfileLorikeet (operating under lorikeetcx.ai, legally Lorikeet CX, Inc., formerly known as Optech) is the premier, enterprise-grade Agentic Customer Experience (ACX) platform, customer service AI pioneer, and automated workflow orchestration powerhouse engineered to serve as the definitive, high-velocity omnichannel resolution, customer intelligence, and digital interaction layer for scaling global brands, fintechs, and highly regulated healthtech systems. Founded by product and AI research visionaries Steve Hind (former product lead at Stripe and Watershed) and Dr. Jamie Hall (former Google Brain research tech lead and co-author on the landmark LaMDA and Meena AI papers), the company completely eliminates the severe systemic friction of modern enterprise customer support—where expanding organizations encounter crippling ticket volumes, high agent overhead, and basic chatbots that merely summarize help centers without solving actual customer issues—by deploying a sophisticated, multi-agent reasoning matrix. Moving far beyond traditional, passive decision-tree scripts or loose retrieval-augmented generation (RAG) models prone to hallucinations, Lorikeet natively unifies an Intelligent Graph architecture that strictly executes complex standard operating procedures (SOPs), multi-turn programmatic workflows, direct secure API action hooks to deep corporate back-ends (like Zendesk, Stripe, Shopify, and on-chain crypto networks), and real-time Voice AI infrastructure into a single high-availability, no-code AI concierge workspace. Solving tickets end-to-end—such as managing delayed e-commerce deliveries, processing multi-step financial refunds, and troubleshooting complex blockchain transactions—the system slashes chat first-response times from 30 minutes to under 60 seconds with 99% execution accuracy. Trusted by customer-obsessed high-growth brands including Airwallex, Linktree, Eucalyptus, Remote.com, and Flex, the platform has scaled rapidly across international footprints. Valued as an elite rising star in the conversational AI landscape, the firm has raised over $49 million in total elite institutional venture capital—anchored by a major Series A growth execution led by QED Investors alongside heavy participation from Square Peg Capital, AirTree Ventures, Blackbird Ventures, King River Capital, Skip Capital, and executives from OpenAI, Stripe, and Atlassian. Under the hood, its technology core utilizes advanced intent-recognition models, real-time multi-agent orchestration loops, and strict role-based access controls designed to isolate risky activities like account cancellations behind dynamic gating parameters without reducing live production velocity. What sets Lorikeet apart is its uncompromising dedication to replacing passive, informational chatbots with absolute real-world resolution predictability, deep system integrations, and multi-channel customer satisfaction; by bridging the gap between performance-intensive structural back-office data and natural, human-quality consumer interactions, the enterprise remains a definitive cornerstone of modern algorithmic customer support infrastructure and global AI business transformation.
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