Agent Lead

Job Description
The Agent Lead sits at the forefront of transforming Financial Advisor productivity through agentic AI workflows, bridging business problems with intelligent automation. This role operates in a high‑ambiguity, rapid experimentation environment, identifying where agent-based approaches can unlock value—and where they should not be applied.

You will define how vendor agents (e.g., CRM‑native), enterprise frameworks, and internally developed agents coexist and interoperate within a governed ecosystem. This is not a pure engineering role—it is a product‑minded builder who shapes, validates, and scales agentic patterns that can be reused across Wealth Management.

The mandate is to move fast, prove value, and establish repeatable patterns while aligning to enterprise architecture, risk, and AI governance standards.

What will you do?
Agentic Strategy & Use Case Qualification

Partner directly with Financial Advisors, field leadership, and business stakeholders to identify high‑value workflow opportunities.

Evaluate when to apply agentic AI versus deterministic automation versus no automation, with the authority to say “this is not an AI problem.”

Define and prioritize agentic use cases aligned to advisor productivity, client engagement, and operational efficiency.

Agent Design, Build & Rapid Experimentation

Lead rapid POC development cycles (fail fast / scale fast) for agentic workflows.

Design multi‑agent interactions across vendor, enterprise, and native agents.

Vendor agents (e.g., CRM/Agentforce)

Enterprise agents (shared services / platforms)

Native/internal agents (event‑driven, workflow‑specific)

Establish reusable agent design patterns (prompting, orchestration, memory, tool usage, escalation paths).

Partner with AI Engineering to validate feasibility, performance, and scalability.

Product Ownership & Lifecycle Accountability

Act as Product Owner for agentic workflows—owning use case shaping through validated solution patterns.

Ensure solutions are not “built and dropped” by defining success metrics, adoption criteria, and driving iteration based on advisor feedback and usage telemetry.

Maintain a portfolio of agentic capabilities with clear value articulation and reuse potential.

Enterprise Alignment & Governance Integration

Engage with enterprise stakeholders to align with approved agentic frameworks and standards and leverage existing capabilities before building new ones.

Ensure all agentic solutions align to model risk governance, data privacy and security requirements, and AI explainability and control frameworks.

Agent Ecosystem & Standards Definition

Define how agent ecosystems operate within RBC Wealth Management, including interaction models between vendor, enterprise, and native agents, guardrails for agent autonomy and decisioning, and cost‑efficiency and performance considerations.

Contribute to evolving enterprise agent standards through applied learnings.

People Leadership & Capability Building

Manage and develop Context Engineers / Prompt Engineers.

Establish best practices in context design, retrieval strategies, and prompt engineering.

Build a culture of experimentation, accountability, and pragmatic problem solving.

Field Engagement & Adoption

Travel (~25%) to branches and field locations to observe advisor workflows, identify friction points and real‑world opportunities for agents, and validate usability and adoption of agentic solutions.

What do you need to succeed?
Must‑have

Proven experience building and deploying agentic AI solutions (multi‑agent systems, orchestration frameworks, tool‑using agents).

Strong understanding of agent frameworks and architectures.

Demonstrated ability to operate as a builder + product owner hybrid.

Experience working across business, engineering, and enterprise governance functions.

Ability to rapidly prototype POCs and iterate based on real user feedback.

Strong judgment in when to use AI versus when not to.

Experience designing workflow‑driven automation.

Leadership experience managing technical talent.

Excellent stakeholder engagement skills.

Nice to have

Experience in Wealth Management / Financial Services.

Familiarity with CRM‑based agent platforms.

Exposure to event‑driven architectures and real‑time data integration.

Understanding of AI risk, model governance, and explainability frameworks.

Experience integrating with enterprise AI platforms.

Background in human‑centered design or workflow optimization.

What’s in it for you?

Direct ownership of one of the most strategically important capabilities in RBC Wealth Management—agentic AI.

Opportunity to define how AI agents fundamentally reshape advisor productivity and client engagement.

High visibility across business, technology, and executive leadership.

Ability to operate in a true “build, test, learn” environment with real‑world impact.

Leadership of next‑generation capability (context engineering + agent orchestration).

Influence on enterprise‑wide agent standards and architecture direction.

Compensation
Salary range: $100,000 - $170,000. Benefits include discretionary bonus, 401(k) with matching, health, dental, vision, life and disability insurance, and paid time‑off.

Location
250 Nicollert Mall, Minneapolis, United States of America

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