Prompt Engineer

Job Description

About the Role

We are seeking a Prompt Engineer who thrives at the intersection of language, logic, and systems thinking. In this role, you’ll design and iterate on prompts that drive reasoning, structured generation, and creative outputs from frontier LLMs. You’ll work directly with researchers, product leads, and developers to fine-tune AI behaviors for specialized use cases in enterprise knowledge systems, educational tools, and applied reasoning.

Key Responsibilities

• Design, test, and optimize prompt templates to guide LLM behavior across diverse domains such as law, medicine, and engineering.
• Construct chain-of-thought, tree-of-thought, and reflection-based prompting structures for multi-step reasoning tasks.
• Translate ambiguous product goals into prompt engineering strategies that are both scalable and interpretable.
• Develop and manage prompt libraries with clear performance metadata (e.g., latency, token usage, model variance).
• Collaborate with model tuning teams to align prompt strategies with finetuned or RAG-augmented models.
• Run evaluations comparing zero-shot, few-shot, and instruction-tuned performance across model families (e.g., GPT-4, Claude, Mistral).
• Build tooling and workflows for internal stakeholders to test and deploy prompt variants without engineering dependencies.
• Document failure modes of prompts under adversarial or out-of-distribution inputs, with actionable recommendations.

Required Qualifications

• 2+ years of experience working directly with LLMs, including prompt design or application-layer development.
• Strong intuition for natural language structure and model behavior, ideally demonstrated with prompt artifacts or shared experiments.
• Deep familiarity with tokenization, logit biases, system messages, and API-level behavior across top model providers (e.g., OpenAI, Anthropic, Cohere).
• Proficiency in Python for prototyping prompts, integrating APIs, and conducting evals with model outputs.
• Track record of working in high-ambiguity environments and producing iterative, testable solutions.
• Ability to write detailed prompt rationales and document experimental findings for cross-functional teams.

Nice to Have

• Experience with few-shot learning design principles, such as example selection and context compression.
• Familiarity with vector databases and RAG workflows, especially prompt tuning for retrieved contexts.
• Exposure to symbolic logic, knowledge graphs, or formal reasoning tools.
• Prior experience working in regulated domains (e.g., finance, healthcare) with prompt safety constraints.

Are you interested in this position?

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

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