Sr. AI Solution Architect

Responsibilities

• Define and maintain the Product Group’s technical vision, architecture roadmap, principles, constraints, policies, and standards.
• Design and guide end-to-end architectures for complex, distributed generative and agentic AI solutions from concept through production.
• Establish reusable patterns for LLMs, Retrieval-Augmented Generation, multi-agent systems, orchestration, integration, security, scalability, reliability, and cost management.
• Lead solution design reviews, provide detailed-design sign-off, and ensure compliance with enterprise architecture and security standards.
• Translate business outcomes and product strategy into technical roadmaps while advising on dependencies, risks, trade-offs, and investment decisions.
• Develop prototypes, proofs of concept, reference implementations, and technical experiments to validate architecture decisions.
• Work directly with engineers on retrieval techniques, agent behaviors, platform capabilities, integration approaches, performance, security, and cost.
• Resolve cross-team architecture issues and integrate solutions with enterprise platforms, shared services, data, and other Product Groups.
• Codify reusable architecture patterns, capabilities, decisions, reference implementations, and lessons learned.
• Mentor colleagues, contribute to knowledge transfer and AI quality engineering practices, and potentially serve as a Chapter Lead.

Requirements

• Undergraduate degree or technical certificate required; graduate degree preferred.
• 10+ years of relevant experience.
• Demonstrated experience leading end-to-end architecture for high-risk, high-complexity, or strategically important solutions.
• Significant AI or machine learning solution experience, including recent hands-on work with generative and agentic AI.
• Experience moving AI solutions from proof of concept into secure, scalable, supportable production use.
• Hands-on experience developing prototypes, reference implementations, or production components to validate architecture decisions.
• Strong knowledge of LLMs, Retrieval-Augmented Generation, vector and hybrid search, grounding, agent architectures, tool integration, and orchestration frameworks.
• Strong understanding of APIs, event-driven integration, distributed systems, and modern software architecture.
• Understanding of semantic and context architectures, data governance, privacy, security, and access controls.
• Knowledge of AI evaluation, benchmarking, observability, responsible AI, solution monitoring, and human review or fallback mechanisms.
• Exceptional collaboration, communication, client-facing, business, architecture, and technical leadership skills.
• Preferred experience includes Microsoft Copilot Studio, Copilot extensibility, commercial and open-source model ecosystems, production multi-agent systems, Azure AI, Google Cloud AI, knowledge graphs, semantic models, infrastructure as code, DevOps practices, AI red teaming, AI security architecture, enterprise AI strategy, and relevant cloud or architecture certifications.

Benefits

• Base salary of $115,600 - $163,200 CAD, with a temporary pay premium that is reassessed annually.
• Health and well-being benefits, savings and retirement programs, paid time off, banking benefits and discounts, career development, and reward and recognition programs.
• Regular development and performance conversations, training programs, an online learning platform, mentoring programs, and onboarding sessions.
• Work location is Toronto, Ontario, Canada, with a 37.5-hour work week.
• Accessibility accommodations are available throughout the interview process.