AI Architect / Toronto, ON (Hybrid) - FTE

AceStack; Toronto; Hybrid; CAD 150,000 - 230,000; Full time

Job summary

AceStack is seeking an experienced AI Architect to lead enterprise-scale AI initiatives, including Generative AI, ML, RAG, and Agentic AI. You will translate business goals into scalable architectures, govern AI programs, and provide technical leadership across engineering and data science teams.

Responsibilities span from design through production, including MLOps, security, governance, and stakeholder communication in a fast-paced environment.

Qualifications

• Must have experience designing and delivering end-to-end AI/Generative AI solutions.
• Strong understanding of enterprise architecture and scalable production deployment.
• Hands-on with LLMs, embeddings, vector databases, and RAG pipelines.
• Experience building agent orchestration and AI guardrails.
• Knowledge of AI governance, privacy, security controls, and regulatory compliance.
• Experience with AI/ML deployment pipelines, observability, and version control.
• Familiarity with human-in-the-loop workflows and responsible AI practices.

Responsibilities

• Own end-to-end AI solution architecture from discovery to production.
• Translate business objectives into AI architecture designs with measurable outcomes.
• Architect enterprise AI solutions integrating foundation models, RAG, and agents.
• Design cloud-native platforms with IAM, secure integrations, and controls.
• Establish governance covering model quality, bias, privacy, and cost optimization.
• Define MLOps/LLMOps practices including deployment pipelines and monitoring.
• Implement responsible AI, model risk management, and security alignment.
• Provide architecture leadership through design reviews and guidance.
• Communicate risks, trade-offs, and business value to stakeholders.

Skills

Enterprise architecture

Solution design

Scalability

Production deployment

Stakeholder management

Architectural documentation

Cross-functional leadership

AI governance

MLOps/LLMOps

Responsible AI

Education

Bachelor's or Master's in CS/Engineering/Data Science/AI

Tools

Azure OpenAI

REST APIs

Vector databases

Semantic search

Containers

Azure AI Services

Job description

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

• 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.

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.
• 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.

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.

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.