Job Title: AI Architect Location: Toronto, ON Work Mode: Hybrid Fulltime FTE - Permanent Experience: 10+ Years Mandatory Experience: 10+ years of overall experience with relevant enterprise AI architecture and Generative AI solution delivery expertise.

Job Summary: We are seeking an experienced AI Architect to lead the end-to-end architecture, design, development, and deployment of enterprise AI solutions, including Generative AI, Machine Learning, RAG, and Agentic AI.

The ideal candidate will translate business requirements into scalable, secure, and production-ready AI architectures while establishing AI governance, evaluation, monitoring, and responsible AI practices.

Key Responsibilities Own end-to-end AI solution architecture, from use-case discovery and feasibility assessment to experimentation, production deployment, and ongoing monitoring.

Translate business objectives into AI solution designs, defining use cases, architecture patterns, decision boundaries, acceptance criteria, and measurable business outcomes.

Design and govern AI solutions encompassing foundation models, RAG, agent orchestration, data sources, vector search, APIs, cloud infrastructure, security controls, and human-in-the-loop workflows.

Establish AI governance, risk management, and evaluation frameworks covering model quality, hallucination, groundedness, security, privacy, responsible AI, compliance, performance, and cost optimization.

Provide technical leadership to engineering and data science teams, review AI implementations, validate solution effectiveness, and ensure architecture standards.

Communicate architecture decisions, technical risks, trade-offs, and recommendations to business and technology stakeholders.

Ensure production readiness through effective monitoring, observability, deployment controls,



and operational support frameworks.

Required Skills and Qualifications 1.

Enterprise AI Solution Architecture Proven experience designing and delivering end-to-end AI, Generative AI, Machine Learning, RAG, and Agentic AI solutions.

Experience taking AI solutions from business use-case discovery through production deployment. 2.

Generative AI, RAG and Agentic AI Solid hands-on knowledge of foundation models and Large Language Models (LLMs).

Expertise in prompt engineering, embeddings, vector databases, and Retrieval-Augmented Generation (RAG).

Experience with tool calling, agent orchestration, and AI guardrails. 3.

Cloud AI Platforms and Integration Architecture Experience with Azure AI Services and Azure OpenAI.

Knowledge of model hosting, API integrations, and event-driven architectures.

Experience with containers, Identity and Access Management (IAM), and scalable cloud-native AI architectures. 4. MLOps, LLMOps and AI Production Operations Experience defining model evaluation frameworks and AI quality metrics.

Knowledge of AI observability, monitoring, deployment pipelines, and model versioning.

Experience with rollback mechanisms, drift detection, and production readiness assessments. 5.

Responsible AI, Security and Governance Understanding of AI governance and model risk management.

Knowledge of privacy engineering, AI security controls, and compliance requirements.

Experience with human-in-the-loop workflows, AI safety, bias and fairness assessments, and responsible AI practices.

Preferred Candidate Profile Strong combination of AI architecture, hands-on Generative AI expertise, and enterprise solution delivery.

Ability to design secure, scalable, reliable, and cost-effective AI solutions.

Excellent technical leadership, architecture governance, stakeholder management, and communication skills.

📌 AI Architect || Toronto, ON || Fulltime FTE
🏢 AceStack
📍 Toronto