Position: AI Architect

Location: Toronto, ON (Hybrid)

Employment Type: Full time

Total Experience: 10 & Above years

Role Description:
Own end-to-end AI solution architecture from use-case discovery, feasibility assessment, experimentation, production deployment, and ongoing operational monitoring.
Translate business objectives into AI solution designs by defining AI use cases, decision boundaries, architecture patterns, acceptance criteria, and measurable business outcomes.
Design and govern AI platforms and solutions across foundation models, RAG, agent orchestration, data sources, vector search, APIs, cloud infrastructure, security controls, and human-in-the-loop processes.
Establish AI governance, risk, and evaluation frameworks covering model quality, hallucination, Groundedness, security, privacy, responsible AI, compliance, performance, cost, and operational readiness.
Provide technical leadership and architecture assurance by guiding engineering and data science teams, reviewing AI implementations, validating solution effectiveness, and communicating architecture decisions, risks, and trade-offs to business and technology stakeholders.

Required Skill Set:

Enterprise AI Solution Architecture





Proven experience designing and delivering end-to-end AI, Generative AI, Machine Learning, RAG, and Agentic AI solutions from use-case discovery through production deployment.

Generative AI, RAG & Agentic AI Expertise

Solid hands-on knowledge of Foundation Models, Prompt Engineering, Embeddings, Vector Databases, Retrieval-Augmented Generation (RAG), Tool Calling, Agent Orchestration, and AI Guardrails.

Cloud AI Platforms & Integration Architecture

Experience with Azure AI Services, Azure OpenAI, Model Hosting, APIs, Event-Driven Integration, Containers, Identity & Access Management, and scalable cloud-native AI architectures.

MLOps / LLMOps, AI Evaluation & Production Operations

Experience defining model evaluation frameworks, observability, monitoring, deployment pipelines, versioning, rollback mechanisms, drift detection, and operational readiness for production AI systems.

Responsible AI, AI Security & Governance

Robust understanding of AI governance, model risk management, privacy engineering, security controls, human-in-the-loop designs, compliance requirements, AI safety, bias/fairness assessment, and responsible AI practices.

📌 Ai Architect Toronto
🏢 Ku0026K Global Talent Solutions
📍 Toronto