Job Description

What's the opportunity?

AI Architecture Governance & Insights establishes enterprise-wide AI architecture standards, patterns, and governance frameworks for RBC. We create the standards and certification criteria that ensure AI solutions are built safely, securely, and in alignment with the enterprise strategy. We deliver insights into our AI capabilities and compliance, and we partner with security, enterprise architecture, and business teams to ensure AI is built responsibly and strategically

As a Lead Data Scientist in the Governance & Insights team, you will build the standards, patterns, and tools that enable RBC's AI transformation at scale. You'll be the architect behind our architecture practice, creating governance artifacts, maintaining CALM/Agent Card standards, supporting the AI Architecture Review Board, and building automation to surface insights across our AI estate.

You’ll work in a quick-paced environment where priorities shift rapidly based on leadership needs, platform evolution, and emerging governance challenges. This role requires both strategic thinking and hands‑on execution. You might pivot from defining reference architectures to building a MCP server to developing compliance dashboards within the same week.

Your responsibilities include:

- Governance & Standards (Core Focus)
- Create and maintain AI governance artifacts: patterns, standards, reference architectures, and technical whitepapers
- Manage CALM standard evolution and its alignment to AI platforms (Athena, Nova, Atlas)
- Define and evolve the AI Technical Reference Model, capability catalog, and license-to-build criteria
- Play a key role in the AI Architecture Review Board,



including preparing materials, reviewing submissions, and contributing architectural opinions
- Represent AI Architecture perspectives at broader governance forums (Technology Infrastructure and Security review boards)
- Track Agent adherence to standards/patterns and report on compliance/non‑compliance

- Architecture Practice & Insights

- Partner with engineering teams to review solution designs and provide architectural guidance
- Deliver AI asset insights: catalog state, capability coverage, compliance metrics, and adoption trends
- Modernize the SafeAI process and integrate with AI platform governance
- Publish Architecture Decision Records (ADRs) for cross‑cutting decisions
- Coordinate cross-pillar architecture decisions and continuously improve certification processes

- Tooling & Automation ("As Code" Mindset)

- Build tools, Agents, and MCPs to automate architecture governance (e.g., CALM Visualizer, SafeAI tooling)
- Surface governance data through AI Architecture Agents
- Translate business and technical problems into implementable designs, then contribute hands‑on development

- Flexibility & Partnership

- Research emerging AI technologies and evaluate vendor solutions
- Partner with EA, LOB governance groups, and GCS on security architecture alignment




- Drive POCs for new AI capabilities and governance approaches
- Work directly with lines of business to understand needs and architect practical solutions

You're our ideal candidate if you have:

- Technical Depth
- Strong grasp of AI technologies (LLMs, agents, RAG, agentic systems, AI infrastructure)
- Hands‑on coding ability. You must be comfortable building tools, automations, and prototypes yourself
- Experience with infrastructure-as-code, API design, and modern development practices

- Architecture Mindset

- Proven ability to design scalable, secure, maintainable systems
- Understanding of platform patterns, reusability, and governance at scale
- Can translate abstract requirements into concrete, implementable designs

- Governance & Standards

- Experience creating technical standards, patterns, or reference architectures
- Understanding of compliance frameworks and how to operationalize governance
- Comfortable working with Architecture Review Boards or similar governance bodies

- Operating Model

- Adaptability: Thrive in fast‑paced environments where priorities shift based on leadership asks and platform evolution
- Parallel execution: Comfortable context-switching between strategic work (standards development) and tactical delivery (tool building, ARB support)
- Partnership: Strong collaboration skills, you'll work across EA, Security, Engineering, and business teams
- Bias for action: Don't wait for perfect information; build, test, iterate

- Passion for AI Governance

- Stay current with internal/external AI trends and innovation
- Genuine interest in making AI technology accessible, well‑govern

📌 Lead Data Scientist (Toronto)
🏢 Royal Bank of Canada
📍 Toronto