Prompt Engineer

Job Description

We’re looking for a Prompt Engineer who thrives at the intersection of language, logic, and code. In this role, you won’t just write prompts — you’ll reverse-engineer cognition. You’ll design, test, and optimize complex prompt architectures for large language models (LLMs), enabling our AI systems to perform domain-specific tasks across legal tech, healthcare diagnostics, and technical writing. You’ll partner with researchers, developers, and product managers to operationalize AI capabilities into reliable workflows. This is not a plug-and-play prompt tuning role — we need someone who treats prompt engineering like a scientific and linguistic discipline.

Responsibilities

• Develop, evaluate, and refine prompt chains, templates, and instruction strategies for tasks including multi-step reasoning, code generation, summarization, and data transformation.
• Design systematic experiments to test prompt variants under different model parameters, temperature settings, and context lengths.
• Translate ambiguous product needs into precise model instructions that are robust, scalable, and reproducible.
• Build and manage internal libraries of reusable prompt modules with thorough documentation and benchmarks.
• Collaborate with fine-tuning and retrieval-augmented generation (RAG) teams to align prompt strategies with model training and memory systems.
• Stay ahead of LLM updates and model behaviors (e.g., GPT-4.5 vs Claude vs Gemini) to adapt prompting strategies as models evolve.
• Mentor other teams in effective prompt usage, including bias mitigation, hallucination detection, and context engineering.

Qualifications

Required

• Demonstrable experience crafting advanced prompts for OpenAI, Anthropic, or open-source LLMs (e.g., Mistral, LLaMA) in production or research settings.
• Deep understanding of token limits, context window management, and LLM response behaviors under various decoding strategies.
• Strong grasp of prompt failure modes — including prompt injection, misalignment, verbosity, and hallucination — and how to mitigate them.
• Proficiency with Python and at least one LLM API (e.g., OpenAI, LangChain, LlamaIndex).
• Ability to design A/B experiments for prompt performance using statistical rigor.
• Experience using tools such as Weights & Biases, Pinecone, VectorDBs, or other LLMOps stacks.
• Exceptional written communication skills with a sharp editorial instinct — you should obsess over phrasing, tone, and ambiguity.

Preferred

• Background in computational linguistics, symbolic logic, information retrieval, or philosophy of language.
• Experience in at least one domain with strict factuality or regulatory standards (e.g., law, medicine, finance).
• Familiarity with fine-tuning models or hybrid architectures using retrieval-augmented generation (RAG).
• Experience writing synthetic data generation scripts for model evaluation or QA tasks.

Are you interested in this position?

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