AI Architect / Toronto, ON (Hybrid) - FTE

Job summary

Lead end-to-end architecture and delivery of enterprise AI solutions, from use-case discovery and design through production deployment and operational monitoring. Establish secure, scalable, governed AI practices and provide technical leadership while communicating architectural decisions, risks, trade-offs, and business value to stakeholders.

Job details

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 • Own end-to-end AI solution architecture, from use-case discovery and feasibility assessment to experimentation, production deployment, and operational monitoring. • Translate business objectives into AI architecture designs, defining use cases, architecture patterns, decision boundaries, acceptance criteria, and measurable business outcomes. • 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. • Design scalable, secure, cloud-native AI platforms with appropriate identity and access management, integration patterns, and infrastructure controls. • Establish AI evaluation and governance frameworks covering model quality, accuracy, groundedness, hallucinations, security, privacy, bias, fairness, compliance, performance, and cost optimization. • Define MLOps/LLMOps practices, including deployment pipelines, model and prompt versioning, observability, monitoring, drift detection, rollback strategies, and production readiness. • Implement responsible AI, AI safety, model risk management, privacy engineering, and security controls aligned with enterprise policies and regulatory requirements. • Provide architecture leadership through technical reviews, design assurance, solution validation, and guidance to engineering and data science teams. • Communicate architectural decisions, technical trade-offs, risks, and business value to senior business and technology stakeholders. Required Technical Skills 1. Enterprise AI Solution Architecture • Proven experience designing and delivering end-to-end AI, Generative AI, Machine Learning, RAG, and Agentic AI solutions. • Strong understanding of enterprise architecture, solution design, scalability, and production deployment. 2. Generative AI, RAG & Agentic AI • Hands-on expertise in foundation models, Large Language Models (LLMs), prompt engineering, embeddings, vector databases, and semantic search. • Experience building RAG pipelines, tool/function calling, agent orchestration, and AI guardrails. 3. Cloud AI Platforms & Integration • Experience with Azure AI Services, Azure OpenAI, model hosting, REST APIs, and enterprise integration architecture. • Knowledge of event-driven architectures, containers, cloud-native infrastructure, and identity and access management. 4. MLOps/LLMOps & Production Operations • Experience implementing AI/ML deployment pipelines, model evaluation, observability, monitoring, and version control. • Understanding of model drift detection, rollback mechanisms, performance optimization, and production support. 5. Responsible AI, Security & Governance • Knowledge of AI governance, model risk management, privacy, security controls, and regulatory compliance. • Experience with human-in-the-loop workflows, hallucination mitigation, bias/fairness assessment, AI safety, and responsible AI practices. Preferred Qualifications • Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or a related field. • Experience delivering enterprise AI solutions within complex, regulated environments. • Strong stakeholder management, architectural documentation, technical decision-making, and cross-functional leadership skills.

What you’ll do

Lead end-to-end architecture and delivery of enterprise AI solutions, from use-case discovery and design through production deployment and operational monitoring. Establish secure, scalable, governed AI practices and provide technical leadership while communicating architectural decisions, risks, trade-offs, and business value to stakeholders.

Requirements

Requires 10+ years of experience and demonstrated expertise designing and delivering enterprise AI, Generative AI, machine learning, RAG, and agentic AI solutions. Candidates should have hands-on experience with LLMs, Azure AI and Azure OpenAI, cloud integration, MLOps/LLMOps, and responsible AI governance; a related bachelor's or master's degree is preferred.

Listed skills

• Machine learning · Preferred
• prompt engineering · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• AI Architecture
• Generative AI
• Machine Learning
• Retrieval-Augmented Generation
• Agentic AI
• Large Language Models
• Prompt Engineering
• Vector Databases
• Azure AI Services
• Azure OpenAI
• MLOps
• LLMOps
• AI Governance
• Responsible AI
• Cloud-Native Architecture
• Identity and Access Management

Job areas

• Technology
• Software
• Data & Analytics
• Management & Leadership

More jobs from AceStack