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Full-stack and applied-AI interview prep (JS/TS) in India

PrepHike · 6 min read

If you build with JavaScript or TypeScript and you have started shipping LLM features, you sit in the most contested hiring lane in India right now. The pay range is wide and the interviews are uneven. This guide covers exactly what full-stack and applied-AI panels probe, so your prep matches the gate.

Why this track is its own thing

A full-stack JS/TS engineer who has also wired up an LLM feature is not interviewed like a plain frontend developer or a plain backend developer. Product and startup panels treat you as someone who can own a slice end to end: the React screen, the Node service behind it, the database, and now a model call that has to behave in production. The salary spread is the widest you will see anywhere. Two engineers with the same three years can sit at 9 LPA and 26 LPA, and the difference is almost never raw coding speed. It is whether you can explain decisions under pressure.

That gap is the whole reason interviews feel unfair. You do the work daily, but the interview asks you to narrate it cleanly in a few minutes. Before you prep answers, get honest about where you actually stand against the market, because the target salary shapes everything else. Our market value audit is the step most underpaid engineers skip, and it is why their prep aims at the wrong band.

The full-stack core panels still test

The applied-AI part gets the attention, but the fundamentals are still where most candidates lose the offer. Panels assume you know React. They are checking whether you understand the cost of what you write.

• JavaScript and TypeScript depth. Expect javascript interview questions on the event loop, microtasks versus macrotasks, closures, and where this binds. On the TS side: when a discriminated union beats an enum, what unknown buys you over any, and why generics on an API client matter. A strong full stack interview in India almost always includes one moment where the interviewer asks "why" three times in a row. Surviving that is the test.
• React beyond syntax. Re-render behavior, why a key prop matters, when useMemo is real and when it is cargo cult, and how you would debug a slow list of 5,000 rows. Server components and data fetching patterns come up at product companies using Next.js.
• Node and APIs. REST versus GraphQL and the honest trade-off: GraphQL solves over-fetching and N+1 client round trips, but you pay for it with caching complexity and resolver depth limits. If you list GraphQL, expect to defend a real schema decision you made.
• Databases. Indexing, when a query goes from 200ms to 8 seconds and why, the difference between a transaction and an optimistic lock, and when Postgres jsonb is the right call versus reaching for Mongo. Panels love the question "you have a slow endpoint, walk me through finding the cause."
• Redis and queues. This is where mid-level candidates separate. Redis as a cache versus a rate limiter versus pub/sub versus streams, eviction policies, and the failure mode when your cache and database drift apart. With queues (BullMQ, SQS, or similar): idempotency, retries, dead-letter handling, and what happens when a worker dies mid-job.

None of this is exotic. The trick is that you have to produce it on demand, with a number and a trade-off attached. Building a structured skill Q&A bank for every line on your resume is how you stop freezing on the obvious follow-up.

The applied-AI layer: where the real premium sits

"I integrated ChatGPT" is on thousands of resumes now. It is worth almost nothing in an interview by itself. What earns the llm engineer interview premium is showing you understand the system you built and its failure modes. Here is what panels actually probe.

LLM integration, done like an engineer

Expect questions on prompt structure, why you split system and user messages the way you did, and how you handle the model returning malformed output. Strong answers mention structured output and schema validation, token budgets and what you do when context overflows, streaming responses to the UI, and cost. If you cannot estimate roughly what a feature costs per 1,000 requests, that is a flag. Interviewers also ask about latency: a model call adds seconds, so how did you keep the interface usable. Caching, optimistic UI, and partial streaming are the credible answers.

RAG, past the buzzword

Retrieval-augmented generation is the most common rag interview questions topic, and the most commonly faked. The panel wants the pipeline: chunking strategy and why your chunk size, embedding model choice, the vector store (pgvector, Pinecone, Qdrant), and the retrieval step. Then the hard part they are really testing: how do you know retrieval is good? Be ready to talk about evaluation, why the model still hallucinates when retrieval misses, reranking, and why naive cosine similarity often returns the wrong chunk. Mention how you handle stale documents and access control on retrieved content, because at a real company not every user can see every document.

Agentic pipelines

Multi-step agents, tool calling, and orchestration come up at startups building on this seriously. The probe is about control, not magic: how you define tools, how you stop an agent from looping forever, how you handle a tool call that fails, and how you keep the whole thing observable when something goes wrong in step four of seven. Honesty wins here. Saying "we tried a full agent loop, it was unpredictable, so we constrained it to three defined steps" reads as senior. Pretending it all worked perfectly does not.

What product and startup panels probe differently

Startups care less about whether you memorized an algorithm and more about whether you ship safely and reason about cost and risk. The questions tilt toward "you have two weeks and this half-built, what do you cut," "this AI feature is wrong 5 percent of the time, do we launch," and "the bill tripled last month, find out why." Product companies layer in scale and reliability: rate limiting against a paid model API, fallback when the provider has an outage, and guarding against prompt injection when user input reaches the model.

When you describe a project, the panel is listening for ownership and judgment, not a feature list. The 30-second project method keeps you from rambling into the weeds, and a clear system design checklist matters even more once an LLM is in the diagram, because now you are designing around a slow, non-deterministic, metered dependency. Capacity estimation, failure handling, and a cost line are no longer optional.

How to prepare so it holds under pressure

Reading this list is not preparation. The gate is whether you can defend every claim in a live round, when an interviewer interrupts and pushes. That is the part you cannot rehearse alone, because you cannot surprise yourself. Two scored mock rounds, one technical and one on your real projects, expose the spots where you sound thin before a real panel does. Our SHIFT method runs exactly this loop: surface your real value, rebuild how your work reads, stress-test every skill, then mock-test until a rubric clears. You can see how the paid steps are staged on the pricing page, starting with a Rs. 199 call and a written report on where you are underpaid.

The engineers in this track are usually not underskilled. They are underpriced because their interview narration has not caught up to their work. Close that gap and the band you compete in moves up fast.