Director of Product
Apply on Hivemind Capital Partners’s site Posted 2d ago · via Lever
Lightyear is building the operating system for enterprise AI consumption: the layer that measures, governs and optimizes how organizations spend on tokens, models and agents. We are hiring a Director of Product, our founding product lead, to own what we build, in what order, and for whom.
The product is modular- Lightyear's basic offering gives finance and engineering leaders attribution of AI spend by developer, model, team and project, empowering them to optimize usage and spend. Advanced tiers are deployed directly into an enterprise's operations, optimizing model routing and budget in real time.
This is a 0-to-1 role. You will define the MVP, recruit and run design partners, set the methodology for attributing token spend and connecting it to work outcomes, carry the technical half of the sale with FinOps leads, platform and AI engineering teams, finance, and the CTOs and heads of AI who must justify the AI budget, and build the product function from scratch. You should be as comfortable debating gateway architecture and telemetry schemas with engineers as you are sitting across from a CFO explaining the return on their AI spend. You will be the founding product lead and first product hire, with a mandate to hire and lead the product and design team as Lightyear grows.
About Lightyear
Lightyear is the operating system for enterprise AI consumption: measured, governed and optimized. Enterprise spend on foundation-model APIs has grown more than tenfold since the start of 2025, yet it remains scattered across invoices and cards, rarely attributed to the people and projects that incur it, and almost never connected to the work it produces. Lightyear gives organizations token-level visibility, policy enforcement and model optimization, from a read-only product that installs without admin keys to an in-path gateway that applies budget and routing rules to every request, and turns the resulting usage data into benchmarks, indices and financial products. A Hivemind Digital Group company, Lightyear pairs its product with Hivemind Capital's asset-management platform and the group's first-hand read on how enterprises adopt and pay for AI.
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, e • 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 produ
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