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About This Role
Job Description
What is the opportunity?
This is a rare leadership role at the intersection of AI product management, domain\-driven data architecture, and wealth management transformation. You will define and own the Data \& AI product portfolio that powers Financial Advisor productivity, client intelligence, and revenue growth.
You will operate as a true Product Owner owning vision, roadmap and outcomes across:
- Data Products (domain\-aligned, reusable, governed assets)
- AI Products (agentic workflows, predictive insights, copilots)
- Data Models (conceptual logical product\-aligned implementations)
This role sits at the center of a modern Data \& AI operating model—bridging Digital Experience, Business, and Technology—with a mandate to build, test, and scale high\-impact capabilities quickly while aligning to enterprise architecture and governance standards.
Success in this role means products are not just delivered—but adopted, trusted and measurably drive business outcomes (e.g., FA productivity, client growth, risk reduction).What will you do?
You will act as both strategic product leader and hands\-on Product Owner, with direct accountability for a portfolio of Data \& AI products.
1\. Own the Data \& AI Product Lifecycle (End\-to\-End)
- Define and execute product vision, strategy, and roadmap across Data and AI domains
- Translate wealth management business needs into scalable, reusable product capabilities
- Drive product lifecycle management: ideation incubation build adoption optimization
- Ensure alignment to domain\-driven data models (Client, Account, Positions, Transactions, etc.)
2\. Act as a True AI Product Owner (Hands\-On)
- Write and prioritize product backlogs across AI, data, and modeling layers
- Define clear product requirements (user stories, acceptance criteria, KPIs)
- Partner closely with AI Engineering, Data Engineering, and Architecture to deliver solutions
- Make trade\-off decisions between vendor capabilities (e.g., agent platforms) and internal builds
3\. Lead Data Product \& Modeling Strategy
- Ensure all products are built on trusted, governed, and reusable data assets
- Drive standardization of data definitions, lineage, and quality across domains
- Partner with Data Modeling and Governance teams to enforce consistency and scalability
- Elevate data from “assets” to operable products (APIs, analytics layers, AI\-ready datasets)
4\. Deliver AI Products that Drive Advisor Productivity
- Own AI use cases such as:
+ Advisor copilots \& agentic workflows
+ Client intelligence \& next\-best\-action
+ Automated insights from transactional and behavioral data
- Ensure AI products are:
+ Explainable
+ Trusted
+ Embedded into advisor workflows (e.g., CRM, Salesforce)
5\. Drive Adoption \& Value Realization
- Define and track product success metrics (usage, productivity lift, revenue impact)
- Ensure products are actively used—not just delivered
- Partner with field leadership and advisors to embed products into day\-to\-day workflows
- Continuously iterate based on feedback and measured outcomes
6\. Lead and Develop a High\-Performance Team
- Manage a team of \~6 (product managers, analysts, or hybrid roles)
- Operate as a player–coach: mentoring while directly owning critical product areas
- Establish strong product management discipline and culture (ownership, accountability, speed)
Must\-Have
- Proven AI Product Ownership experience (ideally from organizations like Meta, Amazon, Block Inc., or Morgan Stanley)
- Demonstrated ability to own and ship AI/ML\-powered products end\-to\-end
- Strong experience in product management disciplines:
+ Roadmapping, backlog ownership, prioritization
+ Defining KPIs and driving measurable outcomes
- Deep understanding of data product concepts:
+ Domain\-driven design
+ Data modeling (conceptual/logical alignment to products)
+ Data quality, lineage, and governance
- Experience building AI\-driven workflows or agentic systems
- Ability to operate as both:
+ Strategic leader
+ Hands\-on individual contributor
- Strong stakeholder management across business, engineering, and architecture
Nice to Have
- Wealth Management or Financial Services experience (advisor workflows, client lifecycle, portfolio insights)
- Experience with CRM platforms (e.g., Salesforce) and AI integrations
- Exposure to knowledge graphs, GraphRAG, or explainable AI frameworks
- Familiarity with modern data platforms (e.g., Snowflake, real\-time \+ analytical integration)
- Background in multi\-agent orchestration or AI platforms
- Experience working in matrixed, regulated environments
What’s in it for you?
- Opportunity to define and lead one of the most strategic capabilities in Wealth Management: Data \& AI Products
- Direct ownership of high\-visibility, high\-impact AI initiatives tied to revenue and client growth
- Ability to shape how AI transforms Financial Advisor workflows and client experiences
- A role that blends:
+ Product leadership
+ Data strategy
+ AI innovation
- High exposure to senior leadership and enterprise strategy
- Build and lead a next\-generation product organization in a rapidly evolving AI landscape
*The good\-faith expected salary range for the above position is $140,000 \- $230,000 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*
Job Skills
AI Product Management, AI Product Management, Analytics, Artificial Intelligence (AI), Artificial Intelligence Strategy, Business, Business Case Design, Critical Thinking, Data Modeling, Data Strategies, Key Performance Indicators (KPI), Knowledge Organization, Leadership, Machine Learning (ML), Product Development Lifecycle, Product Development Methodology, Product Management Leadership, Product Manufacturing, Product Ownership, Product Services, Product Testing, Product Vision, Results\-Oriented, Roadmapping, Value Chain Mapping {\+ 1 more}Additional Job Details
Address:
250 NICOLLET MALL:MINNEAPOLISCity:
MinneapolisCountry:
United States of AmericaWork hours/week:
40Employment Type:
Full timePlatform:
TECHNOLOGY AND OPERATIONSJob Type:
RegularPay Type:
SalariedPosted Date:
2026\-07\-10Application Deadline:
2026\-07\-17Note: *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.
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Salary Context
This $140K-$230K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At RBC, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($185K) sits 15% below the category median. Disclosed range: $140K to $230K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
RBC AI Hiring
RBC has 3 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Minneapolis, MN, US, New York, NY, US, Seattle, WA, US. Compensation range: $145K - $230K.
Location Context
AI roles in Seattle pay a median of $236,900 across 267 tracked positions. That's 9% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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