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About This Role
Job Description
As Director of AI Engineering \& Full\-Stack Development in RBC Capital Markets, you will lead a team of AI engineers and architects responsible for designing, evaluating, and deploying ethical, compliant, and operationally transformative AI solutions. This role combines strategic technology leadership with hands\-on oversight of cutting\-edge AI and full\-stack development initiatives. You will analyze and recommend AI technology stacks, cultivate strategic vendor and technology partnerships, and drive the architectural vision that enables Capital Markets digital innovation while maintaining the highest standards of governance and responsibility.
What is the opportunity?
This leadership role centers on building and scaling an AI engineering capability that transforms operations across RBC Capital Markets. You will oversee the evaluation and adoption of emerging AI technologies, guide your team through complex technical decisions, and ensure all solutions align with RBC's ethical AI principles, regulatory requirements, and risk management frameworks. You will serve as a strategic bridge between business stakeholders, engineering teams, and executive leadership, translating business imperatives into robust technical strategies and fostering a culture of innovation, accountability, and continuous learning.
What will you do?
- Lead and mentor an engineering team through design, development, and deployment of enterprise AI solutions.
- Evaluate and recommend AI technology stacks, architectures, and vendor partnerships aligned with business needs and organizational standards.
- Promote ethical AI governance frameworks, ensuring all solutions incorporate responsible AI principles, bias detection, and compliance mechanisms.
- Partner with business leaders to identify high\-impact automation and AI opportunities and quantify their operational and financial benefits.
- Drive architecture reviews, technical governance, and quality standards across all AI initiatives.
- Build and maintain strategic relationships with AI technology vendors, research institutions, and thought leaders.
- Communicate AI strategy, roadmap, and technical insights to executive stakeholders and engineering teams.
- Lead cross\-functional workshops to define requirements, resolve technical challenges, and align expectations.
- Foster a culture of continuous learning, innovation, and operational excellence within your team.
- Ensure all solutions maintain compliance with regulatory requirements, data governance, and RBC risk management frameworks.
What Do You Need to Succeed?
Must Have:
- Minimum 10\+ years of full\-stack development and 5\+ years of AI/ML engineering experience, with at least 3 years in a leadership or senior technical role.
- Deep expertise in modern AI technologies, including generative AI, LLMs, machine learning frameworks, and related platforms.
- Advanced proficiency in at least one primary programming language (.NET Framework, Python, or equivalent) and demonstrated breadth across multiple platforms.
- Strong experience with API integration, microservices architecture, and cloud\-native development.
- Experience with MCP (Model Context Protocol) servers and emerging AI infrastructure standards.
- Hands\-on understanding of the full development lifecycle, including design patterns, testing frameworks, deployment pipelines, and DevOps practices.
- Demonstrated experience evaluating, selecting, and implementing technology stacks and vendor solutions.
- Proven track record of leading engineering teams, mentoring talent, and building high\-performance cultures.
- Excellent communication skills, verbal, written, and presentation, with the ability to translate complex technical concepts for diverse audiences.
- Strong understanding of ethical AI principles, responsible AI governance, compliance frameworks, and risk mitigation strategies.
- Ability to think strategically about technology investment and business value while maintaining hands\-on technical judgment.
- Bachelor's degree in Computer Science, Engineering, Mathematics, Finance, or equivalent professional experience.
- Exceptional relationship management skills and ability to build trust across the enterprise with stakeholders, partners, and teams.
Nice to Have:
- Experience in capital markets, financial services, or regulated industries.
- Knowledge of technology landscape, architecture standards, or enterprise governance frameworks.
- Familiarity with AI safety, model governance, or responsible AI frameworks
- Background in process automation, workflow optimization, or business architecture.
- Exposure to Agile/Scrum methodologies and experience scaling development practices across teams.
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 position is $160,000 \- $250,000 USD, 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
Job Skills
Additional Job Details
Address:
GOLDMAN SACHS TOWER, 30 HUDSON STREET:JERSEY CITYCity:
Jersey CityCountry:
United States of AmericaWork hours/week:
40Employment Type:
Full timePlatform:
CAPITAL MARKETSJob Type:
RegularPay Type:
SalariedPosted Date:
2026\-08\-11Application Deadline:
2026\-08\-25Note: *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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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.
Salary Context
This $160K-$250K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($205K) sits 5% below the category median. Disclosed range: $160K to $250K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
RBC AI Hiring
RBC has 3 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Jersey City, NJ, US, Minneapolis, MN, US. Compensation range: $170K - $250K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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