Enterprise AI Engineer

Quix; United States; Remote; USD 110,000 - 180,000; Full time

Job summary

Quix seeks an experienced AI/LLM engineering lead to transform AI capabilities into dependable enterprise workflows that align with data, systems, and governance requirements. You will guide model selection, build retrieval-based applications, and evaluate behavior to ensure responsible, production-ready deployments.

You will collaborate with data and engineering teams to ground outputs in organizational information, implement safeguards, and document system behavior for both technical and

Qualifications

• Proven experience delivering production AI or LLM applications beyond prototypes.

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.

Skills

Production AI/LLM apps

Retrieval systems

Model evaluation

Enterprise data integration

AI governance & controls

Tools

Vector stores

Embeddings

Semantic search

Reranking

Job description

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.