Prompt Engineer

Job Summary

We are looking for a Prompt Engineer to define and lead the strategy, patterns, and tooling for designing prompts, agentic workflows, and LLM-based application architectures across the organization.

Key Responsibilities

• Define organization-wide standards, patterns, and reference architectures for LLM-based applications
• Design prompt structures, instruction templates, and retrieval strategies for diverse production use cases
• Architect agentic systems incorporating tool use, planning, memory, and multi-step reasoning
• Lead the design of retrieval-augmented generation pipelines including chunking, indexing, and reranking strategies
• Develop evaluation frameworks for prompt quality, agent reliability, and end-to-end task success
• Build internal tooling and libraries that accelerate LLM application development across teams
• Establish guardrails, safety filters, and policy enforcement patterns for LLM-powered products
• Collaborate with model engineering teams on prompt-model co-design and fine-tuning opportunities
• Mentor engineers and applied scientists on prompt engineering and LLM application architecture
• Conduct technical reviews of LLM application designs across multiple product teams
• Lead red-teaming exercises and continuously improve robustness against adversarial inputs
• Track latency, cost, and quality trade-offs in LLM application design and recommend optimizations
• Document patterns, anti-patterns, and lessons learned for broad internal reuse
• Stay current with LLM capabilities, tooling, and research, and translate advances into practical guidance

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Computational Linguistics, or a related field
• Six or more years of software engineering experience, with significant time on LLM-based applications
• Deep familiarity with modern LLM APIs and agent frameworks
• Strong understanding of retrieval-augmented generation, embeddings, and vector databases
• Experience designing evaluation pipelines for non-deterministic systems
• Solid grasp of responsible AI principles, including safety and policy considerations
• Excellent written and verbal communication skills
• Track record of mentoring engineers and influencing technical direction

Preferred Qualifications

• Public writing, talks, or open-source contributions on LLM application development
• Familiarity with multi-agent architectures and complex tool-use systems
• Experience with fine-tuning workflows and when to choose them over prompting
• Exposure to product domains such as customer support, coding assistants, or analytics agents
• Experience integrating LLMs into enterprise software systems with strict compliance requirements