Director of Product

What you'll do

Product & Platform 

Own the product roadmap and the definition of product-market fit, from MVP through the Pro-to-Enterprise ladder, and decide the build sequence across the module set: spend visibility and attribution, token credit request and approval, usage governance and compliance, model optimization, routing and procurement, outcome and productivity measurement, and data and indices 

Define Lightyear Pro: agents and SDKs export their own telemetry, metadata only (no access to underlying prompts or code), minimal admin credentials, customers keep their existing LLM-provider subscriptions, with usage and cost tracked by developer, model, team and project 

Define Lightyear Enterprise with engineering: gateway and console running single-tenant in the customer's cloud, model and budget rules enforced in the request path, and routing of each task to the model with the highest measured return on cost 

Own the outcome and productivity measurement pillar: join token spend to the work it produced (pull requests merged, tickets resolved, features shipped, support cases closed) through integrations with GitHub, Jira, Linear and similar systems, so customers can see marginal productivity per project, per team and per dollar, not only cost 

Own the measurement methodology: how token spend is attributed across providers and plan types (individual, team, API, enterprise), normalized, and connected to the work it produces, so that "spend productivity" is a number FinOps, finance, engineering leaders and auditors trust; measure efficiency (cost per unit of output) rigorously rather than claim revenue attribution 

Own the token expense-management workflow (request, approval, suggested allocation) as the differentiating hook of the Pro product, with the same ambition that Concur brought to T&E and Deel to payroll 

Define how the data and index layer (inference token benchmarks and indices) is produced from product telemetry, and the product logic for what is published free, licensed, and reserved for Hivemind Capital's financial products 

Treat privacy and security posture as product requirements: metadata-only ingestion, data residency, single-tenant deployment, and the path to enterprise compliance (SOC 2, policy enforcement for corporate and regulatory AI usage rules) 

Commercial, Design Partners & Go-to-Market 

Recruit and run the design-partner program: five to ten organizations already running agents at scale, converting structured conversations with FinOps, platform engineering, finance and engineering leadership (CTO, VP Engineering, Head of AI) into a ranked view of the most acute, monetizable demand and a pilot-to-paid path 

Carry the technical and domain half of the sales conversation across two buyers, the FinOps and platform teams who land the product and the CTO or Head of AI who expands it on outcome measurement: credible demos, integration scoping with platform teams, security and procurement reviews, and the translation of customer needs back into product requirements 

Develop pricing and packaging across the ladder: the free or near-free Pro subscription as a funnel, usage-based Enterprise pricing, and the graduation path between them 

Partner with the sales/BD function and the Hivemind network, opening and running relationships together, with this role owning the technical close 

Serve as "customer zero" by deploying Lightyear on Hivemind Digital Group's own agent spend, and feed what we learn about how enterprises adopt and pay for AI back into the product and the group's investment diligence 

Prepare clear, decision-useful product and commercial reporting for the CEO and group leadership, including PMF signals, design-partner status, pipeline, and go/no-go evidence 

Team 

Hire and lead the founding product and design team, and establish the product discovery, specification and release process with engineering 

Our ideal candidate

7+ years in product management, with at least one product owned end-to-end from 0 to 1 at a developer-infrastructure, FinOps or spend-management, observability, or fintech company
Has shipped products that sit in the request path or ingest telemetry: API gateways or proxies, LLM gateways, observability and APM, cloud cost management, or LLM-ops tooling
Has defined or shipped outcome or productivity metrics for engineering or knowledge work (developer-productivity analytics, AI adoption metrics), or has the rigor to do so with a data lead 
Deep fluency in AI infrastructure economics: provider pricing and plan structures, token metering, model routing, inference costs, and the difference between what individual, team and enterprise plans expose 
Technical enough to be trusted by engineers: reads code, understands SDK and agent instrumentation (OpenTelemetry-style), credential and authentication models, and cloud deployment patterns such as single-tenant and VPC 
Strong commercial instinct, with experience selling to or building for FinOps, platform engineering and finance buyers, and comfort running enterprise security and procurement reviews 
Experience in spend management, expense workflows or FinOps (for example at a corporate-card, cloud cost, or SaaS-management company), or in engineering-intelligence and developer-productivity products, strongly preferred
Working knowledge of data products, benchmarks or indices, and of how usage data becomes a financial product, is a plus but not required
Experience running customer research, design-partner programs, and 0-to-1 commercial motions
Exceptional written and verbal communication, with the ability to make a complex, technical product clear to non-technical buyers
Founder mentality: intrinsically motivated, comfortable with ambiguity, hands-on with specs and demos, and ambitious to grow into broader product leadership as the team scales