Senior Architect

Job Title: Azure AI Architect

Location: Toronto (Onsite)

Type: Contract (CAD$80/hr - $90/hr)

Overview

We are seeking an experienced Azure AI Architect to lead the design and architecture of enterprise-scale Artificial Intelligence (AI) and Generative AI solutions on Microsoft Azure. This role is responsible for translating complex business requirements into secure, scalable, resilient, and cost-effective AI architectures while providing strategic technical leadership across AI initiatives.

The ideal candidate will possess deep expertise in Azure AI services, Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and enterprise cloud architecture. This individual will serve as a trusted advisor to business and technology stakeholders, guiding AI programs from strategy and proof-of-concept through enterprise-scale deployment and operationalization.

Key Responsibilities

AI Architecture & Strategy

• Define and own enterprise AI solution architectures, technology standards, and architectural best practices.
• Develop AI platform strategies focused on scalability, performance, resiliency, security, governance, and cost optimization.
• Establish architectural frameworks, reusable patterns, reference architectures, and technology roadmaps for AI adoption.
• Assess emerging AI technologies, models, and cloud capabilities and recommend solutions aligned to business objectives.
• Ensure alignment with enterprise architecture principles, governance frameworks, and security standards.

Solution Design & Technical Leadership

• Design enterprise-grade AI and Generative AI solutions leveraging Azure AI services and cloud-native technologies.
• Architect Retrieval-Augmented Generation (RAG) solutions, including knowledge ingestion, embeddings, vector search, grounding mechanisms, and access controls.
• Design agent-based AI systems using multi-agent architectures, enterprise integrations, orchestration frameworks, and workflow automation.
• Lead AI model evaluation, selection, and optimization based on accuracy, latency, scalability, security, and operational requirements.
• Architect integrations between AI platforms and enterprise data sources, applications, APIs, databases, and content repositories.
• Drive architecture reviews, design workshops, proofs of concept, and technical assessments.

Security, Governance & Responsible AI

• Define secure AI architecture patterns including identity management, role-based access controls, encryption, API security, and data protection.
• Establish governance frameworks for AI lifecycle management, observability, monitoring, evaluation, and operational excellence.
• Implement Responsible AI principles covering privacy, compliance, transparency, explainability, risk mitigation, and human oversight.
• Ensure architecture designs comply with organizational security policies and regulatory requirements.

Stakeholder Engagement & Collaboration

• Partner with business leaders, data teams, cloud engineers, cybersecurity teams, application developers, and enterprise architects.
• Serve as a senior technical advisor for AI strategy, architecture decisions, and technology investments.
• Present architectural recommendations and solution roadmaps to executive and technical stakeholders.
• Mentor engineering teams and provide guidance on AI architecture best practices, design standards, and emerging technologies.

Required Qualifications

Experience

• Bachelor's degree in Computer Science, Information Technology, Engineering, or a related discipline.
• 12+ years of overall technology experience.
• 5+ years of experience in Solution Architecture, Cloud Architecture, Data Architecture, or Enterprise Architecture roles.
• Proven experience designing and delivering enterprise AI, ML, or Generative AI solutions.
• Experience leading architecture initiatives from concept and proof-of-concept through production deployment.
• Strong track record of collaborating with senior stakeholders and cross-functional engineering teams.

Technical Expertise

• Strong Azure cloud architecture experience.
• Experience with Generative AI, Large Language Models (LLMs), and enterprise AI platforms.
• Hands-on experience designing and implementing RAG-based solutions.
• Experience with AI model evaluation, prompt engineering, observability, and AI governance.
• Strong understanding of APIs, microservices, distributed systems, and enterprise integration patterns.
• Experience with cloud security, identity management, and compliance frameworks.