Senior AI Engineer

Candidates should have experience architecting and implementing end-to-end solutions embedding large language models into core products, designing retrieval-augmented generation workflows, and developing AI-driven features for real-time user assistance. Experience with prompt engineering, building with LLM APIs, and creating tools that augment human experts is required. A strong understanding of ML feature lifecycle management, including prototyping, backend implementation, deployment, and monitoring, is necessary. Proficiency in integrating and optimizing vector databases or embedding stores, and experience with agent-style workflows for AI tool usage are also required. Familiarity with designing scalable AI infrastructure, collaborating with DevOps on microservices and observability, and defining measurable success criteria for AI features is essential. A background in defining evaluation pipelines, including automated regression tests and human-in-the-loop reviews, is expected.