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.
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
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
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.
