Lead Agentic AI Engineer for Financial Advisory

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

Benefits offered by this job

Competitive compensation

401(k) with company-matching

Paid time off

Job summary

RBC Wealth Management in Minneapolis seeks an experienced Agent Lead to advance advisor productivity through agentic AI workflows. This role blends product leadership with hands-on AI engineering to design, validate, and scale agent patterns across vendor, enterprise, and internal agents within a governed ecosystem.

You will own use cases, define success metrics, and drive iteration from advisor feedback and telemetry. Travel up to 25% to branches to observe workflows and adoption.

Qualifications

• Experience with actuarial modeling and data analytics.
• Proficiency in ML/NLP and data-driven decision making.
• Ability to design and evaluate AI-enhanced workflows.

Responsibilities

• Partner with Financial Advisors and stakeholders to identify high-value AI opportunities.
• Lead rapid POC cycles and design multi-agent interactions across vendors and internal agents.
• Act as Product Owner for agentic workflows with defined success metrics and telemetry-driven iterations.
• Establish reusable agent design patterns including prompting, orchestration, memory models, and tool usage.
• Collaborate with enterprise teams to align with approved frameworks and governance.
• Develop Context/Prompt Engineers and champion best practices in context design and agent behavior tuning.
• Travel ~25% to observe workflows and validate usability.

Skills

Actuarial Modeling

Big Data Management

Commercial Acumen

Data Mining

Data Science

Decision Making

Machine Learning

Natural Language Processing

Predictive Analytics

Python

Job description

RBC Wealth Management in Minneapolis seeks an experienced Agent Lead to advance advisor productivity through agentic AI workflows. This role blends product leadership with hands-on AI engineering to design, validate, and scale agent patterns across vendor, enterprise, and internal agents within a governed ecosystem.

You will own use cases, define success metrics, and drive iteration from advisor feedback and telemetry. Travel up to 25% to branches to observe workflows and adoption.