Enterprise AI Engineer

Role overview

Turn AI capabilities into dependable workflows that fit enterprise data, systems, and governance requirements. The role covers the path from selecting an appropriate model and building retrieval-based applications to evaluating behavior and preparing systems for responsible production use.

Responsibilities - Build AI-assisted workflows that connect to enterprise data, APIs, and operational systems. - Develop retrieval-augmented generation pipelines that ground model responses in relevant organizational information. - Create evaluation methods for output quality, reliability, and behavior at system boundaries. - Add safeguards such as access controls, content filtering, audit records, and output validation. - Work with data and engineering partners to establish dependable inputs and integrations. - Document system behavior, limitations, and operating needs for technical and nontechnical audiences. - Compare hosted AI services and open models against use-case needs and risk considerations.

Requirements - Proven experience delivering production AI or LLM applications beyond prototypes. - Strong understanding of retrieval systems, including vector stores, embeddings, semantic search, and reranking. - Ability to assess model and workflow quality, reliability, and failure behavior. - Experience integrating AI components with enterprise data sources, APIs, or business systems. - Understanding of governance and controls needed to operate AI responsibly in organizations.

Benefits and work setup - Full-time role, based in Toronto or Montréal, or remote within Canada.