Job Title: AI Architect
Location: Toronto, ON
Work Model: Hybrid
Experience: 10+ Years
Role Overview
We are seeking an experienced AI Architect to lead the design, development, and implementation of enterprise-scale AI, Generative AI, Machine Learning, RAG, and Agentic AI solutions. The ideal candidate will translate business requirements into scalable, secure, governed, and production-ready AI architectures while providing technical leadership across engineering, data science, and business teams.
Key Responsibilities
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Own end-to-end AI solution architecture, from use-case discovery and feasibility assessment to experimentation, production deployment, and operational monitoring.
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Translate business objectives into AI architecture designs, defining use cases, architecture patterns, decision boundaries, acceptance criteria, and measurable business outcomes.
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Architect enterprise AI solutions integrating foundation models, RAG pipelines, agent orchestration, vector databases, enterprise data sources, APIs, cloud infrastructure, and human-in-the-loop workflows.
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Design scalable, secure, cloud-native AI platforms with appropriate identity and access management, integration patterns, and infrastructure controls.
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Establish AI evaluation and governance frameworks covering model quality, accuracy, groundedness, hallucinations, security, privacy, bias, fairness, compliance, performance, and cost optimization.
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Define MLOps/LLMOps practices, including deployment pipelines, model and prompt versioning, observability, monitoring, drift detection, rollback strategies, and production readiness.
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Implement responsible AI, AI safety, model risk management, privacy engineering, and security controls aligned with enterprise policies and regulatory requirements.
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Provide architecture leadership through technical reviews, design assurance, solution validation, and guidance to engineering and data science teams.
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Communicate architectural decisions, technical trade-offs, risks, and business value to senior business and technology stakeholders.
Required Technical Skills
- Enterprise AI Solution Architecture
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Proven experience designing and delivering end-to-end AI, Generative AI, Machine Learning, RAG, and Agentic AI solutions.
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Strong understanding of enterprise architecture, solution design, scalability, and production deployment.
- Generative AI, RAG & Agentic AI
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Hands-on expertise in foundation models, Large Language Models (LLMs), prompt engineering, embeddings, vector databases, and semantic search.
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Experience building RAG pipelines, tool/function calling, agent orchestration, and AI guardrails.
- Cloud AI Platforms & Integration
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Experience with Azure AI Services, Azure OpenAI, model hosting, REST APIs, and enterprise integration architecture.
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Knowledge of event-driven architectures, containers, cloud-native infrastructure, and identity and access management.
- MLOps/LLMOps & Production Operations
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Experience implementing AI/ML deployment pipelines, model evaluation, observability, monitoring, and version control.
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Understanding of model drift detection, rollback mechanisms, performance optimization, and production support.
- Responsible AI, Security & Governance
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Knowledge of AI governance, model risk management, privacy, security controls, and regulatory compliance.
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Experience with human-in-the-loop workflows, hallucination mitigation, bias/fairness assessment, AI safety, and responsible AI practices.
Preferred Qualifications
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Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or a related field.
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Experience delivering enterprise AI solutions within complex, regulated environments.
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Strong stakeholder management, architectural documentation, technical decision-making, and cross-functional leadership skills.
