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Lightyear is building the operating system for enterprise AI consumption—a platform that measures, governs, and optimizes how organizations spend on tokens, models, and agents. You will be the founding Director of Product and first product hire, owning what gets built, in what order, and for whom.
The product is modular. Lightyear's basic offering (Pro tier) provides finance and engineering leaders with attribution of AI spend by developer, model, team, and project, enabling them to optimize usage and cost. Advanced tiers deploy directly into enterprise operations, optimizing model routing and budgets in real time.
This is a 0-to-1 role with significant scope. You will define the MVP, recruit and run a design-partner program with five to ten organizations already running agents at scale, establish the methodology for attributing token spend and connecting it to work outcomes, carry the technical half of sales conversations with FinOps leads, platform and AI engineering teams, finance leaders, CTOs, and heads of AI, and build the product function from scratch. You will be equally comfortable debating gateway architecture and telemetry schemas with engineers and explaining return on AI spend to CFOs.
Key responsibilities include:
**Product & Platform:** Own the product roadmap and definition of product-market fit from MVP through the Pro-to-Enterprise ladder. Define Lightyear Pro (agents and SDKs export telemetry, metadata-only, minimal admin credentials, usage tracked by developer/model/team/project) and Lightyear Enterprise (single-tenant gateway and console in customer's cloud, model and budget rules enforced in request path, intelligent routing). Own the outcome and productivity measurement pillar—joining token spend to work produced (pull requests merged, tickets resolved, features shipped, support cases closed) through integrations with GitHub, Jira, Linear, and similar systems. Establish the measurement methodology for attributing spend across providers and plan types, normalized and connected to work produced. Own the token expense-management workflow (request, approval, suggested allocation) as the Pro product's differentiating hook. Define how the data and index layer (inference token benchmarks and indices) is produced from product telemetry. Treat privacy and security as product requirements: metadata-only ingestion, data residency, single-tenant deployment, and the path to enterprise compliance (SOC 2, policy enforcement).
**Commercial, Design Partners & Go-to-Market:** Recruit and run the design-partner program, converting structured conversations with FinOps, platform engineering, finance, and engineering leadership into a ranked view of monetizable demand and a pilot-to-paid path. Carry the technical and domain half of sales conversations across two buyer personas: FinOps and platform teams (who land the product) and CTOs or heads of AI (who expand it on outcome measurement). Develop pricing and packaging across the ladder: free or near-free Pro as a funnel, usage-based Enterprise pricing, and the graduation path between them. Partner with sales/BD, opening and running relationships with this role owning the technical close. Serve as "customer zero" by deploying Lightyear on Hivemind Digital Group's own agent spend, feeding learnings back into product and investment diligence. Prepare clear, decision-useful product and commercial reporting for the CEO and group leadership.
**Team:** Hire and lead the founding product and design team. Establish the product discovery, specification, and release process with engineering.
Requirements:
- 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
- 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
- Defined or shipped outcome or productivity metrics for engineering or knowledge work (developer-productivity analytics, AI adoption metrics), or 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 differences between individual, team, and enterprise plans
- Technical fluency trusted by engineers: reads code, understands SDK and agent instrumentation (OpenTelemetry-style), credential and authentication models, cloud deployment patterns (single-tenant, VPC)
- Strong commercial instinct with experience selling to or building for FinOps, platform engineering, and finance buyers; comfort running enterprise security and procurement reviews
- Experience in spend management, expense workflows, or FinOps (corporate-card, cloud cost, SaaS-management companies), or engineering-intelligence and developer-productivity products (strongly preferred)
- Working knowledge of data products, benchmarks, or indices, and how usage data becomes a financial product (preferred but not required)
- Experience running customer research, design-partner programs, and 0-to-1 commercial motions
- Exceptional written and verbal communication; ability to make complex technical products clear to non-technical buyers
- Founder mentality: intrinsically motivated, comfortable with ambiguity, hands-on with specs and demos, ambitious to grow into broader product leadership as team scales
