Lakshya

Artificial Intelligence

Applied track Fastest-growing

2 · Agent / AI Product Engineer

Named in 64% of all job descriptions. The centre of gravity of the whole market.

What the job actually is

You build product features where a model takes actions — calling tools, holding state across turns, recovering from its own mistakes. The hard part is not the prompt. It is everything around it: what happens when the third tool call in a chain returns garbage, how you keep a multi-turn session inside a context budget, how you stop a retry loop from costing $400, and how you prove a change made things better rather than merely different.

Titles this hides behind

Software Engineer, AgentsAI Agent Engineer Frontier Agents EngineerAI Engineer Software Engineer, AI PlatformSDE III — Gen AI Software Engineer, AI ProductAgent Builder

Who is hiring it

Effectively everyone — this is the archetype that spread furthest beyond AI-native companies. Sierra, Glean, Writer, Cursor, Notion, Linear, Vanta, Ramp, Postman, Atlan, plus the whole frontier-lab product surface. In India: Postman, Sarvam, Observe.AI, Tekion, Meesho.

How to recognise it in a JD

Look for agentic, tool use, function calling, orchestration, multi-turn, guardrails, and increasingly MCP. The tell that separates it from archetype 3 is that the metrics named are product metrics — task completion, resolution rate, deflection — rather than model metrics.

The loop

Recruiter screen What you have actually shipped that a user touched.
Practical coding Frequently "build a small agent" rather than an algorithms puzzle. An IDE, real API docs, 60–90 minutes.
LLM system design — the decisive round RAG fundamentals are the entry fee, not the bar. The real question starts one level up: what happens when the system you built breaks in a way no tutorial covers.
Product sense Where a model should and should not be in the loop, and what quality bar ships.
Behavioural At senior and staff level, interviewers pick three to five topics and drill into failure modes and what went wrong last time.

Prepare like this

  • Ship one agent with real tool calling and a real eval harness. Not a notebook — something with retries, timeouts, structured logs, and a regression suite you can run before and after a prompt change.
  • Break it deliberately and write down what happened. Kill a tool mid-chain. Return malformed JSON. Overflow the context. The interview is largely about failure modes, and you cannot narrate failures you have not seen.
  • Learn MCP properly while it is still a differentiator at 8% JD penetration and climbing.
  • Get fluent in cost and latency arithmetic — tokens per request, cost per resolved task, p95 versus p50 when a chain has four sequential calls.
  • Have a position on prompt injection and what you actually do about it when the agent has write access to something.

Be ready for

  • "Your agent calls a tool that fails silently and returns an empty result. How do you detect it?" The best answers reach for evals and tracing, not for a better prompt.
  • "How do you evaluate a multi-turn agent, where there is no single correct output?" The archetype-defining question. See archetype 6.
  • "The context window fills up mid-task. Now what?" Summarisation, retrieval, handoff, or task decomposition — and the cost of each.
  • "You changed a prompt and the demo looks better. Do you ship it?" Correct answer involves a golden set and a regression run, not a vibe.

Red flags in the posting

No mention of evaluation anywhere. A team building agents without evals is shipping on vibes and will blame you when quality regresses invisibly.

A framework named as the job. "Experience with LangChain required" as a headline requirement usually means the team has confused a library with an architecture.

Compensation

United States — base$170k – $300k Wide, because the title spans product teams at $170k and frontier labs well past $300k.
India — total₹30L – ₹65L Estimated. A 20–40% premium over generalist ML roles is widely reported for hands-on LLM work.

What this role tests

Themes, and where to learn them

These chapters are shared across every role that tests them, so preparation here compounds rather than being spent once.

Measuring what resists measurementNamed in 59% of AI postings and 42% of platform postings. Almost nobody studies it deliberately.READ THE CHAPTER →Latency and cost arithmeticThe one piece of maths that is genuinely counterintuitive — and it comes up in every agent interview.READ THE CHAPTER →The controlOne habit separates strong candidates from plausible ones more reliably than any technical depth.READ THE CHAPTER →

Practice questions across all themes →  ·  Back to Artificial Intelligence →