Agent Lead

ODAIA; Minneapolis (MN); On-site; USD 100,000 - 170,000; Full time

Benefits offered by this job

401(k) with company matching

Health, dental, and vision insurance

Paid time-off plan

Job summary

ODAIA is looking for an Agent Lead to enhance Financial Advisor productivity through innovative AI solutions. In this role, you will partner with advisors to identify high-value workflows while managing a team of Context Engineers to test and scale agentic approaches.

This position requires a strong blend of product ownership and engineering insight to drive successful implementation and adoption of AI-powered solutions designed to boost engagement and operational efficiency.

Qualifications

• Proven experience building and deploying agentic AI solutions.
• Strong understanding of agent frameworks and architectures.
• Leadership experience managing technical talent.
• Excellent stakeholder engagement skills.

Responsibilities

• Partner with Financial Advisors to identify workflow opportunities.
• Lead rapid POC development cycles for agentic workflows.
• Act as Product Owner for agentic workflows.
• Engage with enterprise stakeholders for alignment.

Skills

Agentic AI solutions

Understanding of agent frameworks

Ability to prototype

Judgment in AI application

Stakeholder engagement

Job description

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 equally, 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 (“side-of-desk”) to identify high-value workflow opportunities
• Evaluate when to apply agentic AI vs. deterministic automation vs. 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 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 and adoption criteria
  • 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 (e.g., Borealis / enterprise AI, architecture, and platform teams) to:
  • Align with approved agentic frameworks and standards
  • Leverage existing enterprise capabilities before building net new
• Ensure all agentic solutions align to:
  • Model risk governance
  • Data privacy and security requirements
  • 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
  • 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 and retrieval strategies
  • Prompt engineering and agent behavior tuning
• Build a culture of experimentation, accountability, and pragmatic problem solving

Field Engagement & Adoption

• Travel (~25%) to branches and field locations to:
  • Observe advisor workflows firsthand
  • Identify friction points and real-world opportunities for agents
  • 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 (e.g., orchestration layers, memory models, tool integration, event-driven agents)
• 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 vs. when not to
• Experience designing workflow-driven automation (not just models)
• Leadership experience managing technical talent (e.g., prompt/context engineers)
• Excellent stakeholder engagement skills—comfortable working "side-of-desk" with advisors and executives

Nice to have

• Experience in Wealth Management / Financial Services, particularly advisor workflows
• Familiarity with CRM-based agent platforms (e.g., Salesforce Agentforce)
• 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 (e.g., internal AI platforms, cloud AI services)
• 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 a next-generation capability (context engineering + agent orchestration)
• Influence on enterprise-wide agent standards and architecture direction

The good-faith expected salary range for the above position is $100,000 - $170,000 depending on factors including but not limited to the candidate’s experience, skills, registration status; market conditions; and business needs. This salary range does not include other elements of total compensation, including a discretionary bonus and benefits such as a 401(k) program with company-matching contributions; health, dental, vision, life and disability insurance; and paid time-off plan.

RBC’s compensation philosophy and principles recognize the importance of a highly qualified global workforce and plays a critical role in attracting, engaging and retaining talent that:

Drives RBC’s high performance culture

Enables collective achievement of our strategic goals

Generates sustainable shareholder returns and above market shareholder value