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.
