New opportunity

Product Head, Voice AI

Exotel Techcom Pvt Ltd · Bengaluru, India

About this role

Lead Voice AI as a product and business at Exotel, whose AI agents answer and place calls, use LLMs, integrate with customer systems and complete tasks. Own the strategy, product direction, commercial outcomes and economics for agents already used by paying customers in production. The role is based in Bengaluru, Karnataka, India and is full-time.

Responsibilities

Identify promising customer segments, use cases and competitive advantages; use customer conversations, deals, usage and losses to decide where to invest and what to build, buy, partner for or leave aside.
Set product direction for end-to-end agent workflows, reusable features, customer-specific work and system integrations. Own conversation quality, task completion, latency and reliability, including trade-offs between model quality and cost.
Build the business plan with go-to-market and Finance teams. Work with Sales on strategic opportunities, pricing and commercial decisions; own revenue outcomes while balancing growth and profitability.
Own unit economics, cost-per-minute targets and contribution margins by use case or customer. Address inference and infrastructure costs through decisions about models, routing, caching and architecture.
Spend substantial time with customers and prospects, including calls, demonstrations and reviews of successful and failed conversations. Turn market feedback into product, pricing and positioning decisions.
Lead a self-contained pod across Product, Engineering, Delivery and Forward-Deployed Engineering, from customer problem through production agent to measurable outcome. Convert implementation learnings into reusable capabilities to reduce custom work and speed deployment.

Candidate profile: Exotel ideally seeks someone who has launched an AI product used by paying customers, taken a product from 0→1 or early scale, owned revenue, gross margin, P&L or other meaningful business metrics, and made pricing and product-business trade-offs. Direct customer and Sales work, cross-functional leadership and autonomous execution are important. Particularly relevant experience includes Voice AI, LLM applications, real-time AI, speech-to-text, text-to-speech, inference economics, CPaaS, contact centers, telephony, enterprise SaaS, usage-based pricing and forward-deployed teams.

Success after a year is measured through revenue growth, gross margin, cost per minute, net revenue retention, time to production, task completion and forward-deployed engineering hours per customer, alongside better quality, reliability and scalability.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Voice AIConversational AILarge Language ModelsProduct strategyProduct managementGo-to-market strategyPricingUnit economicsP&L managementCustomer discoveryCross-functional leadershipReal-time AI systemsSpeech-to-TextText-to-SpeechLLM inferenceModel routingCachingInference optimizationTelephonyCPaaSContact centerEnterprise SaaSUsage-based pricingForward-deployed engineering

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Choose where Voice AI competes
The role owns segment and use-case selection, competitive positioning, and build, buy or partner decisions. Customer conversations, deals, usage and losses should shape the strategy before the roadmap.
Own product and conversation outcomes
Set direction for end-to-end agent workflows and customer-system integrations. The role is accountable for task completion, conversation quality, latency and reliability in production.
Deliver commercial results
Build the business plan with GTM and Finance, work with Sales on strategic opportunities, and set pricing and deal guardrails while balancing revenue growth with profitability.
Improve per-minute economics
Own cost-per-minute targets and contribution margin by use case or customer. Model choice, routing, caching and architecture are product decisions that affect both customer value and cost.
Scale the pod beyond custom delivery
Lead Product, Engineering, Delivery and Forward-Deployed Engineering from customer problem to measurable production outcome. Turn implementation patterns into reusable capabilities that reduce FDE hours per customer and time to production.

Likely challenges to think through

Quality versus speed and cost
Preparation hypothesis: Better models may improve task completion but increase per-minute cost or latency. Prepare a decision framework for when higher model spend is justified, using conversation outcomes and contribution margin rather than model quality alone.
Strategic deals versus scalable product
Preparation hypothesis: A valuable customer request may require bespoke integration work that slows other deployments. Prepare criteria for accepting custom work, turning repeated needs into product capabilities, or declining a deal; relate the choice to FDE hours, time to production and margins.
Investment choices in a fast-moving market
Preparation hypothesis: Improving models and new competitors can change which use cases are attractive before a roadmap is complete. Prepare to explain how customer wins and losses, usage, and economics would trigger investment, partnership or a decision to stop.

Practice your answers

Technical practice

How would you decide which Voice AI segments and use cases to pursue?

What to coverSuggested preparation: Explain how you would compare customer value, production usage, deal outcomes, losses and competitive advantage. Show how that evidence would determine investment priorities before setting a roadmap.

When would you build, buy, partner for or leave aside a Voice AI capability?

What to coverSuggested preparation: Compare strategic differentiation, speed, cost and the ability to deliver a reliable customer outcome. Explain what evidence would cause you to revisit the decision as models and competitors change.

How would you define an end-to-end phone-agent workflow that connects to a customer system?

What to coverSuggested preparation: Start with the customer task, the information the agent needs and what constitutes completion. Address integration boundaries, unsuccessful outcomes and which parts should be reusable rather than customer-specific.

How would you investigate an agent that interrupts callers or responds too slowly?

What to coverSuggested preparation: Describe how you would review affected conversations and isolate where latency or turn-taking problems arise across the voice-agent workflow. Explain how you would test a fix against conversation quality, task completion and cost.

How would you measure whether production agents are improving?

What to coverSuggested preparation: Define measures for task completion, conversation quality, latency and reliability, using both successful and failed calls. Explain how you would distinguish a genuine customer outcome from a superficially successful conversation.

How would you construct a unit-economic model for usage-priced Voice AI?

What to coverSuggested preparation: Break down cost per minute and contribution margin by use case or customer, including inference and infrastructure costs. Show how you would connect cost targets to pricing, usage and gross margin rather than optimizing cost in isolation.

When is a more expensive model justified for a voice agent?

What to coverSuggested preparation: Compare task success and customer value against latency and incremental inference cost. Discuss where routing, caching or a different architecture might improve the trade-off, and how you would verify the result in production.

How would you set pricing and commercial guardrails for a strategic opportunity?

What to coverSuggested preparation: Relate expected usage and customer value to cost per minute, contribution margin and any customer-specific work. Explain when a deal is worth pursuing and when its economics or demands should change the proposal.

How would you tell whether the Voice AI business is ahead of or behind plan?

What to coverSuggested preparation: Connect revenue growth and net revenue retention to gross margin, cost per minute, time to production and task completion. Explain how you would use differences by segment, use case or account to decide what to change.

When should a customer implementation become a reusable product capability?

What to coverSuggested preparation: Look for recurring needs across customers and compare the investment with forward-deployed engineering hours and deployment time. Explain how you would decide what remains custom, what becomes product and what should not be built again.

Behavioural practice

Tell me about a time you changed or stopped an initiative after new evidence emerged.

What to coverSuggested preparation: Use a real situation, the evidence you gathered and the decision you made despite competing views. Close with the result and what it changed about subsequent investment decisions.

Describe a time you led product and delivery teams through an ambiguous customer problem.

What to coverSuggested preparation: Set out the situation, how you gave direction and removed dependencies across teams, and the outcome. Make your own actions distinct from the team's collective work.

Tell me about a customer conversation or failed product interaction that changed your direction.

What to coverSuggested preparation: Describe what the customer needed, what you learned from the interaction and the action you took. State the resulting product, pricing or positioning change and its observed effect.

Describe a strategic deal where you had to balance growth with product or margin concerns.

What to coverSuggested preparation: Explain the opportunity, the commercial or implementation trade-off and how you worked with Sales or other stakeholders. Give the decision and result without presenting an unmeasured outcome as a success.

Tell me about taking an AI product toward production use by paying customers.

What to coverSuggested preparation: Describe the starting point, your ownership, the decisions needed to launch or scale, and the measurable result. If your example differs from Voice AI, distinguish what transfers to this role from what remains an inference.

Sharpen your knowledge

Topics to prepare

Where Voice AI can win
The role decides which segments and use cases merit investment, using customer conversations, deals, usage and losses. As preparation, compare two hypothetical use cases on customer value, competitive advantage and evidence needed before committing; this is a revision exercise, not an Exotel strategy.
End-to-end voice agent experience
Exotel’s agents answer and place calls, use LLMs, integrate with customer systems and complete tasks. Revise how workflow design, interruptions, task completion, latency and reliability interact; explain which trade-off you would investigate first in a hypothetical failed call.
Voice AI unit economics
The posting calls for cost-per-minute targets and contribution margin by use case or customer. Trace how model choice, inference, routing, caching and infrastructure can affect cost, quality and latency; identify when a more expensive model might justify its cost.
Revenue, pricing and business outcomes
This is business ownership, not roadmap ownership: the role works with GTM, Finance and Sales on a business plan, pricing and commercial guardrails. Revise the relationships among revenue growth, gross margin, usage-based pricing and net revenue retention, then explain a hypothetical growth-versus-profitability decision.
Turning customer implementations into product
The role distinguishes reusable capabilities from customer-specific work and aims to reduce forward-deployed engineering effort. For a hypothetical request recurring across customers, reason through whether to productize, deliver once or decline it, and how the choice affects deployment time and margins.

Skills to revise

Synthesize market evidence
Practice writing a one-page investment recommendation from a hypothetical set of customer requests, deal outcomes, usage and losses. State what to pursue, defer or investigate. This mirrors the posting’s expectation that market evidence drives strategy and roadmap decisions.
Diagnose a failed conversation
Using a hypothetical call transcript, mark interruptions, mistakes, delays and the point at which task completion failed. Propose a testable change and a measure of improvement. The role owns conversation quality, latency, reliability and task completion.
Build a per-minute cost model
Draft a simple calculation separating usage revenue from model, inference and infrastructure costs; compare contribution margin across two hypothetical use cases. This directly practices the cost-per-minute and margin decisions named in the posting.
Frame a commercial decision
Prepare a hypothetical strategic-deal brief with a proposed price, commercial guardrails, expected usage and reasons to accept or decline. The role works with Sales on opportunities while balancing revenue growth and profitability.
Direct a cross-functional delivery decision
Write a short decision brief for Product, Engineering, Delivery and Forward-Deployed Engineering: define a customer problem, the path to a production agent, a measurable outcome and what implementation learning should become reusable. This reflects the pod’s stated end-to-end ownership.

Your preparation checklist

Prepare a production-product walkthrough
The role ideally seeks experience launching AI products used by paying customers. Prepare one example showing the customer problem, your decisions, launch evidence, business results and a trade-off you revisited. If you lack a directly comparable example, label the closest one clearly.
Draft a Voice AI market thesis
As an interview exercise, choose a few plausible customer segments and call workflows. Explain how customer conversations, deal outcomes, usage and losses would help you decide where to invest, partner or decline.
Sketch an end-to-end agent workflow
Prepare a one-page design for an agent that answers or places a call, uses an LLM and customer-system integrations, and completes a task. Show how you would assess interruptions, errors, task completion, latency and reliability; label design choices as proposals.
Build a sample per-minute economics model
The role owns cost-per-minute targets and contribution margin by use case or customer. Bring a worksheet with explicitly hypothetical pricing and cost inputs, plus scenarios for model choice, routing, caching and quality-versus-cost trade-offs.
Rehearse a commercial decision
Prepare a case showing how you weighed customer value, pricing, revenue and margin with Sales or Finance. Identify the evidence that led you to pursue, change or reject an opportunity, without implying it reflects Exotel’s current deals.
Map implementation work into reusable product
Outline how a Product, Engineering, Delivery and Forward-Deployed Engineering pod could move from a customer problem to a production agent. Show criteria for making a customer-specific integration reusable and for reducing deployment time and FDE hours per customer.
Bring questions about the operating baseline
Ask how Exotel currently segments Voice AI use cases, measures task completion and conversation quality, and tracks cost per minute, margin, time to production and customer expansion. Ask where custom delivery most limits scale.

Availability last checked: 5 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

KEEP EXPLORING

Similar AI jobs

Related skills and specializations in India. Matched to this role, not your profile.

Explore more jobs

Finding similar opportunities…