Job description

Job Description What is the opportunity? The Agent Lead sits at the forefront of transforming Financial Advisor productivity through agentic AI workflows—bridging business problems with intelligent automation. This is a high-ambiguity, rapid experimentation role where you ll define how vendor agents, enterprise frameworks, and internally developed agents coexist and interoperate within a governed ecosystem. This is not a pure engineering role—it s a product-minded builder role that shapes, validates, and scales agentic patterns reusable across Wealth Management. What will you do? Partner directly with Financial Advisors, field leadership, and business stakeholders to identify high-value agentic workflow opportunities and evaluate when AI is the right solution vs. deterministic automation Lead rapid POC development cycles with authority to fail fast / scale fast, designing multi-agent interactions across vendor agents (CRM/Agentforce), enterprise agents, and native/internal agents Act as Product Owner for agentic workflows—owning use case shaping through validated solution patterns, defining success metrics, and driving iteration based on advisor feedback and usage telemetry Establish reusable agent design patterns including prompting strategies, orchestration, memory models, tool usage, and escalation paths in collaboration with AI Engineering Engage with enterprise stakeholders (Borealis, architecture, and platform teams) to align with approved agentic frameworks, standards, and governance requirements Manage and develop Context Engineers/Prompt Engineers, establishing best practices in context design, retrieval strategies, and agent behavior tuning Travel (~25%) to branches and field locations to observe advisor workflows, identify friction points, validate usability, and drive adoption of agentic solutions What do you need to succeed? Must-have: 8–10 years total engineering experience — with 2–3 years specifically building agentic or LLM systems (not just prototypes) Hands-on RAG architecture — chunking tradeoffs, retrieval failures, evaluation Built or extended tool integration layers connecting LLM agents to external systems Strong Python backend — FastAPI, async, Pydantic, streaming responses Proven experience building and deploying agentic AI solutions (multi-agent systems, orchestration frameworks, tool-using agents) Strong understanding of agent frameworks, architectures, memory models, tool integration, and event-driven agents Demonstrated ability to operate as a builder product owner hybrid with strong judgment on when to use AI vs. when not to Experience designing workflow-driven automation and working across business, engineering, and enterprise governance functions Leadership experience managing technical talent and excellent stakeholder engagement skills for side-of-desk collaboration Nice-to-have: Experience in Wealth Management or Financial Services, particularly with advisor workflows Familiarity with CRM-based agent platforms (Salesforce Agentforce) and event-driven architectures Understanding of AI risk, model governance, explainability frameworks, and human-centered design What s in it for you: We thrive on the challenge to be our best, progressive thinking to keep growing, and working together to deliver trusted advice to help our clients thrive and communities prosper. We care about each other, reaching our potential, making a difference to our communities, and achieving success that is mutual. A comprehensive Total Rewards Program include competitive compensation and flexible benefits, such as 401(k) program with company-matching contributions, health, dental, vision, life, disability insurance, and paid-time off. Leaders who support your development through coaching and managing opportunities. Ability to make a difference and lasting impact. Work in a dynamic, collaborative, progressive, and high-performing team. Opportunities to do challenging work. Opportunities to build close relationships with clients. The expected salary range for this particular position is $100,000 - $170,000, depending on your experience, skills, and registration status, market conditions and business needs. You have the potential to earn more through RBC’s discretionary variable compensation program which gives you an opportunity to increase your total compensation, provided the business meets its performance targets and you meet your individual goals. 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 LI-POST TECHPJ Job Skills Actuarial Modeling, Big Data Management, Commercial Acumen, Data Mining, Data Science, Decision Making, Machine Learning (ML), Natural Language Processing (NLP), Predictive Analytics, Python (Programming Language) Additional Job Details Address: 250 NICOLLET MALL:MINNEAPOLIS City: Minneapolis Country: United States of America Work hours/week: 40 Employment Type: Full time Platform: WEALTH MANAGEMENT Job Type: Regular Pay Type: Salaried Posted Date: 2026-08-07 Application Deadline: 2026-11-20 Note : Applications will be accepted until 11:59 PM on the day prior to the application deadline date above Our Employment Opportunities At RBC, we are guided by living shared values of Client First, Integrity, Collaboration, Respect and Excellence and winning together as One RBC. We believe an inclusive workplace that has diverse perspectives is core to our continued growth as one of the largest and most successful banks in the world. Maintaining a workplace where our employees feel supported to perform at their best, effectively collaborate, drive innovation, and grow professionally helps to bring our Purpose to life and create value for our clients and communities. RBC strives to deliver this through policies and programs intended to foster a workplace based on respect, belonging and opportunity for all. Join our Talent Community Stay in-the-know about great career opportunities at RBC. Sign up and get customized info on our latest jobs, career tips and Recruitment events that matter to you. Expand your limits and create a new future together at RBC. Find out how we use our passion and drive to enhance the well-being of our clients and communities at jobs.rbc.com . RBC is presently inviting candidates to apply for this existing vacancy. Applying to this posting allows you to express your interest in this current career opportunity at RBC. Qualified applicants may be contacted to review their resume in more detail.

Requirements

Must-haves

What is the opportunity?
What will you do?
Partner directly with Financial Advisors, field leadership, and business stakeholders to identify high-value agentic workflow opportunities and evaluate when AI is the right solution vs. deterministic automation
Lead rapid POC development cycles with authority to fail fast / scale fast, designing multi-agent interactions across vendor agents (CRM/Agentforce), enterprise agents, and native/internal agents
Act as Product Owner for agentic workflows—owning use case shaping through validated solution patterns, defining success metrics, and driving iteration based on advisor feedback and usage telemetry
Establish reusable agent design patterns including prompting strategies, orchestration, memory models, tool usage, and escalation paths in collaboration with AI Engineering
Engage with enterprise stakeholders (Borealis, architecture, and platform teams) to align with approved agentic frameworks, standards, and governance requirements
Manage and develop Context Engineers/Prompt Engineers, establishing best practices in context design, retrieval strategies, and agent behavior tuning
Travel (~25%) to branches and field locations to observe advisor workflows, identify friction points, validate usability, and drive adoption of agentic solutions
What do you need to succeed?
8–10 years total engineering experience — with 2–3 years specifically building agentic or LLM systems (not just prototypes)
Hands-on RAG architecture — chunking tradeoffs, retrieval failures, evaluation
Built or extended tool integration layers connecting LLM agents to external systems
Strong Python backend — FastAPI, async, Pydantic, streaming responses
Proven experience building and deploying agentic AI solutions (multi-agent systems, orchestration frameworks, tool-using agents)
Strong understanding of agent frameworks, architectures, memory models, tool integration, and event-driven agents
Demonstrated ability to operate as a builder product owner hybrid with strong judgment on when to use AI vs. when not to
Experience designing workflow-driven automation and working across business, engineering, and enterprise governance functions
Leadership experience managing technical talent and excellent stakeholder engagement skills for side-of-desk collaboration
Leaders who support your development through coaching and managing opportunities.
Ability to make a difference and lasting impact.

Nice-to-haves

Experience in Wealth Management or Financial Services, particularly with advisor workflows
Familiarity with CRM-based agent platforms (Salesforce Agentforce) and event-driven architectures
Understanding of AI risk, model governance, explainability frameworks, and human-centered design

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmpythonmachine learningml

Platform: workday
Effort: long

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)