Sarvam AIProduct Manager, On-Device & Edge AI interview preparation

A product design question where the obvious feature, answering homework questions, works against the user's real goal. Strong answers design for learning, for the exam, and for the phone the student actually has.

A principal-level design and strategy question. Every default assumption (literacy, a good phone, fast data, English, a credit card) fails. The answer has to pick a job worth solving and an economic model that survives.

Shopkeepers want to know where their money is, in their language, without opening a dashboard. Start with answers that need no permission, add one-tap confirmed actions, and keep refunds and credit behind explicit approval.

Repeat items are predictable; meal plans are a nice demo. Start with replenishment, expose ordering to outside assistants with one confirmation, and keep household preferences inside your own app.

Translation is the smallest part of multilingual AI UX. The interviewer wants script choice, code-mixing, voice, cultural norms, and honest handling of languages where quality is weaker.

Faster speech can make a conversation take longer. Diagnose premature turns, design recovery after interruption and protect actions when a caller changes their mind.

Reasoning traces often leave out what drove the answer, and explanations make people accept wrong answers more. Show progress and evidence, and decide who gets the full trace.

A high-stakes strategic question where the answer is usually no, but not always. The interviewer wants the real goal behind the request, the economics, and a recommendation the CEO can accept.

A market-entry question where copying the US playbook fails. The interviewer wants segments, languages, pricing that works with inference costs, distribution and the competitive landscape.

Sovereignty wins procurement meetings, not renewals. Compete on local languages and voice, deployment control and open weights, use the public sector as the first channel, and track commercial revenue outside the subsidy.

A trade-off question where both sides have serious arguments. The interviewer wants the dimensions that decide it, and a recommendation that depends on facts you would go and get.

A question about fairness and unit economics hiding in a tokenizer. The interviewer wants the mechanism, the arithmetic, and options that do not leave non-English users with a worse product.

Translation through English helped low-resource Indian languages and hurt Hindi in one benchmark. Route by language, protect what must not be translated and measure on your own customers' messages.

Twenty thousand calls a day, two languages and a telephone network you do not control. Scope the intents, budget every millisecond and price the handoff before you promise containment.

A principal-level design question with money, regulation, fraud and a dozen languages in one queue. The answer that scores separates complaint types by what the agent may do, and keeps every money decision in deterministic systems.

A translated English eval set can miss code-switching, transliteration and local support needs. Decide what to sample in each language and who can judge the replies.

Benchmarks die from leakage, label errors and saturation. Hold back a private split, audit labels with experts, score abstention, watch the public-private gap and publish when you will retire it.

A principal-level question because the cost of serving a user is the same in Mumbai and Minneapolis while the price you can charge is not. The free tier is an acquisition budget, and the paid tier needs a model mix by market.

India's Digital Personal Data Protection framework is now in force in phases. The interviewer wants the principles turned into product requirements, stated as of a date and checked with counsel.

English-only evaluation hides regressions that land on most of the world's users. The interviewer wants a plan that covers languages by importance, uses native evaluation, and gates release on per-language results.

Sovereign deployments combine product, infrastructure, security, policy and commercial questions. Principal answers separate what the customer needs (control, residency, continuity, local language) from what they ask for, and design a deployment model the company can sustain.

Synthetic data is cheap and fast, but provider terms, inherited errors and narrow coverage decide whether it helps. Check the contract first, then design the data and the evaluation.

Leaked keys are common and costly. Refund once with conditions, then change the defaults: scanning and automatic revocation, narrow key scopes, spending limits for new accounts and prepaid options.

Data for free usage is a legitimate trade if it is explicit. Draw a hard line so production apps serving regulated users move to paid, and keep enterprise excluded by default.

Prototypes tested in English hide most of what goes wrong for users in other languages. The interviewer wants a prototype cycle that tests real speech and text from target users, with native evaluation.

Decide which support tasks an agent can safely own. An 82% pass rate needs a denominator, a breakdown of failures and a comparison with today's service.

A classic AI product sense prompt. The trap is a chat box bolted onto a grid. The strong answer picks a user, runs every job through an AI-fit test, and treats a silently wrong number as the main risk.

The question behind every AI product decision: a repeatable test, applied step by step, and the willingness to say 'a rule does this better' when it does, and 'a model, with a reviewer' when it does not.

A growth prompt with a hard constraint. Three engineers rules out most ideas, and doubling usage also doubles the compute bill. The interviewer wants a funnel diagnosis and two or three bets that fit.

Tenfold growth is not an optimisation problem. The interviewer wants to see you find new users the product does not serve today, and protect reliability and margins while doing it.

Often the first question in an AI PM loop. Generic answers ('AI is uncertain') score poorly; concrete changes to specs, testing, cost and launches score well.

Explain the task a product handles well, compare the current alternative and defend one improvement with evidence from actual use.

Small sellers have little time, uneven digital skills and thin margins. The strong answer finds the two jobs where AI changes their week, and avoids an assistant that sounds helpful and does nothing.