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AI Prompt Engineer

AI Noida, Uttar Pradesh, India Full-time 2 to 5 years

Design the prompts, eval suites and guardrails that decide whether an LLM feature is good enough to ship.

01
The role

Posted 09-Sep-2026. Applications are paused.

Prompting is engineering here, not trial and error. You will own the prompt and evaluation layer of real AI products: agents, retrieval pipelines and in-product copilots that run against live traffic. The job is to make model behaviour measurable and repeatable, so the team can change a model or a prompt and know, with numbers, whether the system got better or worse.

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02
Responsibilities

AI Prompt Engineer responsibilities

  • Design, version and test prompts for production agents, RAG systems and copilots, and document why each change moved the metric.
  • Build evaluation suites that catch regressions before users do, with graded test sets and clear pass and fail thresholds.
  • Tune retrieval and context assembly so the model gets the right information, not just more of it.
  • Write guardrails and fallbacks for the cases that break a demo: refusals, hallucinations, jailbreaks and long-tail inputs.
  • Work with the engineers serving the model on latency, cost per call and token budgets.
  • Turn what you learn into reusable prompt patterns and eval templates the wider AI practice can pick up.
03
Requirements

AI Prompt Engineer requirements

  • 2 to 5 years working with LLMs in a hands-on role, with shipped or near-shipped features you can talk through.
  • Strong Python, and comfort reading and writing code around an API rather than only using a playground.
  • A real understanding of evaluation: you can explain how you would prove a prompt change is an improvement, not a vibe.
  • Familiarity with RAG, embeddings and vector search, and the failure modes each one brings.
  • Precise written reasoning, since most of this job is making model behaviour legible to other engineers.
Nice to have
  • Experience with eval tooling such as Ragas or Braintrust, and tracing tools for agent runs.
  • Hands-on time with LangGraph or a similar orchestration framework.
  • A background in linguistics, applied ML or data science.
04
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05
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