AI Product Pricing

Role- AI Product Pricing

Location- Noida & Hyderabad

Experience- 12-17 Years

Role Summary:

Define and maintain the pricing, packaging, and unit-economics model for the product — including the cost-to-serve of AI inference — and specify how metering, entitlement, and billing must be built. This role sits at the intersection of product finance and product spec-writing: the output is not just a spreadsheet, it's a PRD-quality specification the CTO org builds against and that this role later validates delivery against.

Key Responsibilities:

• Build and maintain the pricing model and margin analysis per SKU/tier, including sensitivity analysis on key cost drivers
• Model AI cost-to-serve — tokens, compute (GPU/CPU), storage — and quantify its effect on gross margin at different usage tiers
• Specify metering, entitlement, and billing requirements as a PRD-grade document for the CTO org to build against (not a slide deck — a spec with edge cases, definitions, and acceptance criteria)
• Validate delivered metering/billing against the spec; own reconciliation of billed vs. actual usage and flag discrepancies
• Benchmark competitor pricing, packaging, and unit economics for comparable AI-native products; identify what structurally differentiates their pricing model (e.g., seat-based vs. consumption vs. hybrid, included-usage thresholds, overage mechanics)

Must-Have Requirements:

• 10+ years in pricing analytics, product finance, or monetization strategy, with at least one example of owning a pricing model end-to-end (not just contributing analysis)
• Advanced modeling in Excel/SQL/Python, with assumptions that are documented and defensible under questioning — be ready to walk through one model live
• Solid grounding in SaaS and consumption-based pricing mechanics: overage, tiering, commitment discounts, blended vs. marginal cost
• Can write a PRD-quality specification, not just an analysis deck — must be able to produce a work sample or walk through a prior spec
• Working understanding of tokenization cost mechanics (input/output token pricing, context-window cost scaling, caching effects on marginal cost) sufficient to model cost-to-serve for an LLM-based feature
• Has done competitive analysis specifically on AI-native products pricing and packaging, and can articulate what makes one structurally different from another (not just cheaper/more expensive)
• Practical experience using an LLM (ChatGPT, Claude, Copilot, or similar) in the pricing/PRD workflow itself — e.g., to model scenarios, draft spec language, or summarize competitor filings — with a specific example ready to discuss

Preferred:

• Exposure to LLM/GPU cost modeling at a systems level (e.g., understands inference batching, spot vs. on-demand GPU pricing)
• Prior hands-on work with billing systems (Stripe, Zuora, Chargebee, or equivalent) or revenue-assurance processes

Domain Expertise (Relevant Business Vertical):

Demonstrates a strong understanding of at least one relevant domain such as Customer Experience, Supply Chain, Marketing, or Enterprise Finance, and can effectively apply that knowledge to business scenarios and solutions.