AI Prompt Engineer

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

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.

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.

  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.

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