AI Product Pricing

Role- AI Product Pricing Location- Noida & HyderabadExperience- 12-17 YearsRole 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 driversModel AI cost-to-serve — tokens, compute (GPU/CPU), storage — and quantify its effect on gross margin at different usage tiersSpecify 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 discrepanciesBenchmark 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 liveSolid grounding in SaaS and consumption-based pricing mechanics: overage, tiering, commitment discounts, blended vs. marginal costCan write a PRD-quality specification, not just an analysis deck — must be able to produce a work sample or walk through a prior specWorking 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 featureHas 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 discussPreferred: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 processesDomain 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.