Conversational AI Engineer

• 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.

Preferred Qualifications

• 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.