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Product Head, Voice AI

Exotel Techcom Pvt Ltd · Bangalore

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Collected role description

Role overview

Product Head, Voice AI at Exotel Techcom Pvt Ltd

We are looking for someone who can turn this technology into a large, profitable business.

Particularly relevant experience Voice AI / Conversational AI LLM applications Real-time AI systems Speech-to-Text / Text-to-Speech LLM inference economics Model routing/caching/optimisation CPaaS / Contact Centre / Telephony Enterprise SaaS AI products with usage-based pricing Working with Forward-Deployed Engineering / Delivery teams This Role May Be a Strong Fit If You Are A Product leader who has genuinely owned a business, not just a roadmap.

Responsibilities

  • You will own where we play, what we build, who we sell to, how we price, how we scale, and ultimately whether the business wins.
  • You will: Build the business plan with GTM and Finance.
  • Work directly with Sales on strategic opportunities.
  • You will: Build and own the unit-economic model.
  • Drive down inference and infrastructure costs.
  • Build a High-Agency Pod You will lead a self-contained Voice AI pod comprising: Product Engineering Delivery Forward-Deployed Engineering The pod owns the journey from: Customer problem → Solution → Production Voice Agent → Measurable outcome Your job is to give the team direction, raise the quality of decision-making and remove dependencies that slow execution.
  • You will decide: What FDEs should build for a customer.

Employment details

  • Location: Bengaluru [Karnataka] [India]

About the employer

  • About Exotel Exotel is a Customer Engagement Platform for businesses, built for interactions handled by both people and AI agents.

Additional role details

  • We provide the stack behind those interactions - voice, SMS, WhatsApp, RCS and the telephony infrastructure underneath it.
  • Our customers use Exotel to sell, support, collect payments, resolve issues and serve millions of their own customers.
  • Voice AI is a major part of where this is going.
  • Our Voice AI agents answer and place phone calls, reason with an LLM, integrate with customer systems and complete tasks.
  • Customers are already running them in production and paying us by the minute.
  • The market is moving fast.
  • Models are getting better and cheaper, latency is falling and new competitors appear constantly.
  • We need someone who can build the business at that pace.
  • Build and run Voice AI as a business Exotel is building the infrastructure behind customer conversations across voice, SMS, WhatsApp, RCS and AI agents.
  • Voice AI is becoming a major part of this business.
  • Our AI agents answer and place calls, reason using LLMs, connect with customer systems and complete real-world tasks.
  • Customers are already running these agents in production and paying us based on usage.
  • The market is moving extremely quickly.
  • Models are getting better and cheaper, latency is falling, and competition is increasing.
  • This is not a traditional Product leadership role.
  • What You Will Own 1.
  • Strategy - Decide Where We Win Build a clear view of: Which customer segments and use cases are worth pursuing.
  • Where Voice AI can create meaningful business value.
  • Where we have a genuine competitive advantage.
  • Where we are behind the market.
  • What we should build, buy, partner for or ignore.
  • Which opportunities are attractive enough to invest in.
  • You will continuously test this thesis through customers, deals, product usage and losses.
  • The roadmap should be a consequence of this strategy - not the starting point. 2.
  • Product - Build What Customers Will Pay For Set the product direction for Voice AI.
  • Decide: Which workflows agents should handle end-to-end.
  • Which capabilities should become core product features.
  • What should remain customer-specific.
  • How agents integrate with customer systems.
  • When to use the best model versus the most economical model.
  • What should be built internally versus sourced externally.
  • You own the customer experience.
  • If an agent is slow, unreliable, interrupts unnecessarily, makes mistakes or fails to complete a task - you own the problem. 3.
  • Business - Own the Number You will own the business outcome.
  • Determine where revenue should come from by segment, use case and account.
  • Set pricing and commercial guardrails.
  • Decide which deals are worth pursuing.
  • Understand why the business is ahead or behind plan.
  • Balance growth with profitability.
  • You should be equally comfortable discussing product metrics and revenue numbers. 4.
  • Economics - Know What Every Interaction Costs Voice AI economics are fundamentally different from traditional SaaS.
  • Model choice, inference, routing, caching, infrastructure, latency and architecture directly impact margins.
  • Define cost-per-minute targets.
  • Understand contribution margin by use case/customer.
  • Make quality vs latency vs cost trade-offs.
  • Decide when higher model cost is justified by higher customer value.
  • The cost of a minute is a product and business decision. 5.
  • Customers & GTM - Stay Close to the Market You will spend significant time with customers and prospects.
  • You should be comfortable: Joining strategic customer calls.
  • Running important product demonstrations.
  • Understanding customer workflows.
  • Listening to successful and failed conversations.
  • Working with Sales on important opportunities.
  • Understanding why we win and lose.
  • Turning market feedback into product, pricing and positioning decisions.
  • The expectation is not to sit behind the product team and wait for customer feedback.
  • You are expected to be in the market. 6.
  • You should be comfortable operating with high autonomy and limited structure. 7.
  • Turn Customer Work Into Product Early customers will require significant hands-on implementation.
  • Your job is to continuously reduce this dependency.
  • What should become a reusable product capability.
  • What should never be built again.
  • Which patterns across customers deserve product investment.
  • A key measure of success is: Less custom work → Faster deployment → Better margins → More scalable growth.
  • What We're Looking For We care more about what you have actually owned than your previous title.
  • You have ideally: Built and launched an AI product used by paying customers in production.
  • Taken a product from 0→1 or early scale and materially influenced its direction.
  • Owned revenue, gross margin, P&L or meaningful business metrics.
  • Personally made pricing or commercial decisions.
  • Built or deeply understood product unit economics.
  • Worked directly with Sales and customers on important deals.
  • Led cross-functional Product and Engineering teams.
  • Operated with significant autonomy and incomplete information.
  • Made difficult product/business trade-offs.
  • Changed or killed initiatives when evidence showed they were not working.
  • A GM / Business leader with deep product and technology understanding.
  • A Founder / early-stage leader who has built an AI product and taken it to customers.
  • An AI product leader who has worked closely with GTM, customers, pricing and unit economics.
  • This is probably not the right role if: Your experience is primarily roadmap execution.
  • You have worked on AI technology but haven't shipped products to paying customers.
  • You have limited exposure to revenue, pricing or business economics.
  • You prefer large, well-defined organisations and established processes.
  • You are primarily an engineering leader without product/customer/business ownership.
  • What Success Looks Like After a Year Voice AI is a better business because you ran it.
  • We know where to compete and where not to.
  • The product strategy is clear, and the roadmap follows from it.
  • Customers reach production faster.
  • Agents complete more of the jobs they are given.
  • Conversation quality, latency and reliability improve materially.
  • Cost per minute falls, and gross margins improve.
  • Custom work decreases as FDE learnings become product capabilities.
  • The pod operates with high autonomy.
  • Revenue grows, and customers continue to expand their usage.
  • The numbers should confirm the story: Revenue growth | Gross margin | Cost per minute | NRR | Time to production | Task completion | FDE hours/customer