Prompt Engineer

Job Description

Role Overview

We’re looking for a Prompt Engineer with demonstrated experience designing, evaluating, and refining prompts for large language models (LLMs) used in production workflows. This is not a research-only role — we expect a deep understanding of LLM behavior, prompt programming strategies, and a track record of translating them into user-facing systems or decision pipelines.

Key Responsibilities

• Develop and iterate on high-performing prompts across diverse tasks (e.g., summarization, chain-of-thought reasoning, structured extraction, classification) with GPT-4, Claude, and open-source LLMs.
• Construct few-shot and zero-shot prompt configurations informed by corpus-specific constraints and context window limits.
• Diagnose LLM failures using prompt injection testing, adversarial phrasing, and misalignment auditing.
• Maintain a prompt evaluation framework using both quantitative benchmarks (BLEU, ROUGE, exact match) and human-in-the-loop systems.
• Partner with product and engineering teams to A/B test prompt variants and analyze regression in performance due to model or system updates.
• Maintain a version-controlled prompt repository with rigorous documentation and test cases.
• Contribute to the design of custom tools (e.g., UI interfaces or internal DSLs) to empower domain experts to test and refine prompts without needing deep ML backgrounds.

Required Qualifications

• At least 2 years of experience working with LLMs in a practical setting (prompt design, fine-tuning, or evaluation).
• Proficiency in conducting experiments with OpenAI’s API, Anthropic’s Claude, or similar LLMs.
• Deep familiarity with context window management, token limits, and prompt injection vectors.
• Fluency in tools such as LangChain, LlamaIndex, or custom orchestration frameworks.
• Comfortable writing Python and using APIs for experimentation, logging, and prompt benchmarking.
• Strong communication skills and ability to explain prompt logic to non-technical stakeholders.

Preferred Experience

• Familiarity with structured output enforcement via JSON schemas or function-calling features.
• Experience designing prompts that control tone, style, or persona for customer-facing applications.
• Exposure to retrieval-augmented generation (RAG) pipelines and prompt tuning within that context.
• A published portfolio of LLM prompt experiments, breakdowns, or insights (blog posts, GitHub repos, etc.).

Are you interested in this position?

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

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