AI Architect / Toronto, ON (Hybrid) - FTE

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

1. 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.

2. 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.

3. 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.

4. 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.

5. 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.