Prompt Engineer

Job Description

We’re seeking a Prompt Engineer who thrives at the intersection of language, logic, and product design. You will work closely with product, design, and engineering teams to craft highly specific prompt structures that enable LLM-based systems to perform reliably across a variety of tasks — from reasoning over structured data, to emulating subject matter expertise in narrow domains, to dynamically generating long-form content with controlled tone and structure.

You will build and refine prompt pipelines, develop test suites to evaluate prompt performance, and collaborate with engineers to ship LLM-powered features into production. Your success will be measured not just by prompt effectiveness, but also by your ability to articulate prompt behaviors, edge cases, and limitations.

Responsibilities

• Design, implement, and test advanced prompt strategies for diverse use cases (e.g., decision trees, few-shot prompting, role-conditioning, embedded instruction patterns).
• Create and maintain evaluation datasets for prompt effectiveness, bias detection, hallucination frequency, and model alignment with business logic.
• Pair with engineers to turn brittle prototypes into resilient production-grade pipelines.
• Collaborate with product managers to translate ambiguous user-facing goals into precise LLM instructions.
• Run A/B tests and latency vs. quality tradeoff experiments on different model configurations (GPT-4, Claude, open-source models).
• Document prompt behavior systematically — including context length effects, memory interference, and temperature-driven variability.
• Monitor model drift and update prompts accordingly with the arrival of new model versions or underlying changes.

Qualifications

Required:

• Deep familiarity with prompt engineering techniques: chain-of-thought, few-shot, zero-shot, system-role design, and guardrail injection.
• Demonstrated experience in designing prompts that drive consistent and structured output across variable inputs.
• Fluency in evaluating LLM behavior using qualitative methods and structured test cases (e.g., rubric-based scoring, regression analysis).
• Comfort with Python (especially for prompt templating and logging test outputs).
• Ability to read between the lines of product specs and turn vague intent into deterministic LLM behavior.
• Strong writing and editing skills — you’ll need to fine-tune tone, style, and structure across multiple prompt layers.

Are you interested in this position?

Apply by clicking on the “Apply Now” button below!

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