Sarvam AI Applied AI Engineer interview questions

When a drug name or a Hindi word becomes nine subword tokens, you pay in cost, context, and accuracy. Adding tokens is easy and adding them well is not. Here is the tradeoff.

The modal Applied AI design round. Anyone can draw embed-retrieve-generate. The signal is in chunking, hybrid retrieval, the rerank/latency tradeoff, and how you prove it works. Here is the structure that scores.

Agents are over-applied. The strong answer resists the hype: most tasks want a single structured call, and agents earn their cost only under specific conditions. Here is when to reach for one and how to stop it from compounding errors.

The two knobs that make or break a RAG system, and the ones candidates hand-wave. The signal is tuning chunking against recall and knowing exactly what dense retrieval misses that BM25 catches. Here is the reasoning, not the rules of thumb.

Applied AI rarely waits for certainty, and companies (Amazon's Bias for Action, NVIDIA's speed) test whether you can act decisively without freezing or being reckless. The signal is a reversible, well-reasoned call with a plan to validate. Here is the arc that lands.

Enterprise RAG fails on isolation, not retrieval quality. The signal is enforcing tenant and document-level permissions server-side, at retrieval time, so the model can never surface data a user cannot see. Here is the design that survives a security review.

Moderation is a multi-stage classification problem with brutal tradeoffs: false negatives cause real harm, false positives censor legitimate users, and the adversary adapts. The signal is the tiered pipeline, per-severity precision/recall calibration, and human-in-the-loop. Here is the design.

Applied AI is half translation. Interviewers ask this to see whether you can drop the jargon, frame tradeoffs in business terms, and earn trust with people who control the roadmap. Here is how to show that skill in the answer itself.

Most RAG systems ship with no real eval, which is why most RAG systems quietly degrade. The signal is decomposing evaluation into retrieval and generation, and measuring faithfulness separately from relevance. Here is the framework that makes RAG improvements measurable.

This is two questions hiding as one: how would you have caught it before full rollout, and how do you debug it now. Answer both and you signal senior judgment. Here is the staged-rollout and root-cause playbook.

Extreme class imbalance plus a hard latency budget plus an adversary who adapts. The signal is handling imbalance honestly, choosing the operating point from costs, and designing for the feedback loop. Here is the end-to-end design.

The interviewer wants real numbers, not 'use a big GPU.' Weights are fixed, the KV cache grows with load, and the lever order decides everything. Here is the back-of-envelope and the serving stack.

The interviewer wants you to know why a model that fits on no single GPU still trains, and to do the optimizer-state memory math that motivates sharding. Here is the parallelism taxonomy and the 16-bytes-per-parameter calculation.

Quantization is the first lever for fitting and speeding up models, and the interviewer wants more than 'use fewer bits.' The signal is knowing what precision buys you, why outliers break naive quantization, and how you prove quality held. Here is that answer.

A favorite at hardware-aware shops. The signal is understanding that attention's cost is memory traffic, not FLOPs, and that FlashAttention is an exact, IO-aware reordering, not an approximation. Here is the answer that shows you think about the memory hierarchy.

The KV cache is why LLM serving is hard, and the interviewer wants the mechanics: what it stores, why it limits concurrency, and the tricks that shrink it. Here is the answer that shows you understand decode-time economics.

Decode is sequential and memory-bound, so latency tricks matter. The signal is explaining speculative decoding's draft-and-verify mechanism (and why it stays exact) plus the other levers and when each applies. Here is the latency toolkit.