AI Architect

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

Strong 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

Strong 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.