Prompt Engineering Architect
- 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.
- Conduct technical reviews of LLM application designs across multiple product teams.
- Mentor engineers and applied scientists on prompt engineering and LLM application architecture.
- 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.
- 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.
- Demonstrated experience shipping LLM-powered products to production.
- 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.
- Strong Python skills and comfort with modern application frameworks.
- 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.
- Public writing, talks, or open-source contributions on LLM application development.
- Experience with multi-agent architectures and complex tool-use systems.
- Familiarity 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.
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