About Exotel

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

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.

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

This is not a traditional Product leadership role.

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.

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.

You will:

• Build the business plan with GTM and Finance.

• Determine where revenue should come from by segment, use case and account.

• Work directly with Sales on strategic opportunities.

• 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.

You will:

• Build and own the unit-economic model.

• Define cost-per-minute targets.

• Understand contribution margin by use case/customer.

• Drive down inference and infrastructure costs.

• 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. 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 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.

You will decide:

• What FDEs should build for a customer.

• 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.

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.

• 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