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AI Solutions Architect
Volaris Group
Remote, CAN
Full-time
Senior
Remote
Discovered 3 weeks ago
Large Language Models (LLMs)Retrieval-Augmented Generation (RAG)Google CloudMicrosoft CopilotOpenAIAnthropic
Free
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Large Language Models (LLMs)Retrieval-Augmented Generation (RAG)Google Cloud
Key Skills for This Role
Large Language Models (LLMs)Retrieval-Augmented Generation (RAG)Google CloudMicrosoft CopilotOpenAIAnthropic
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The Role
As AI Solutions Architect, you will partner to define the technical architecture, standards, and reusable solution patterns that enable our businesses to adopt and operationali z e AI. This is a senior technical leadership role focused on enterprise AI architecture rather than traditional software development.
Working alongside Business CEOs, Product Leaders, engineering teams, AI Champions, and AI Pods, you will design scalable AI solutions that accelerate product innovation, automate business processes, and improve customer outcomes while keeping them secure, maintainable, and aligned with enterprise architecture principles. You will support AI Work Pods with architectural guidance, technical coaching, and reusable frameworks, while each business retains ownership of its own implementation.
Key Relationships
Head of Artificial Intelligence : Reporting to and escalation .
AI Transformation Lead : Matrix to align technical architecture with transformation delivery.
Business CEOs, Product Leaders, AI Champions, and AI Pods across participating businesses.
Product Experts, engineering teams, and Security, Infrastructure, and Engineering functions, on delivery, enterprise standards, and Responsible AI.
Key Responsibilities
- In this role you will:
- Define and maintain best practices for the Portfolio AI Reference Architecture, its guiding principles, and reusable, secure solution patterns that align with existing software platforms and technology strategies.
- Evaluate and recommend enterprise AI platforms / technologies and build reference implementations using leading AI tools.
- Set enterprise standards and engineering best practices across the AI stack, spanning model evaluation, testing and validation, observability, performance monitoring, and version management.
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