AI Engineer - Full Stack Agentic UX

📍 Job Overview

Job Title: AI Engineer - Full Stack Agentic UX

Company: RBC

Location: Toronto, Ontario, Canada (with potential for Vancouver, British Columbia, Canada)

Job Type: Full Time

Category: Technology & Engineering (AI/ML Focus)

Date Posted: August 11, 2026

Experience Level: 5-10 Years (Estimated)

Remote Status: On-site

🚀 Role Summary

• Design and develop sophisticated human-agent interaction patterns for enterprise AI platforms, focusing on progressive autonomy, intent previews, and explainable rationale to foster user trust and control.

• Build full-stack AI-driven capabilities that integrate with an orchestration layer to automate complex workflows, strategically balancing human-AI coordination for optimal task completion.

• Implement robust trust and transparency mechanisms, including confidence signals, audit trails, and escalation pathways, to ensure users understand AI actions and reasoning within a regulated financial services environment.

• Develop enterprise integrations that connect AI functionalities with critical corporate systems such as project management, collaboration tools, source control, and communication platforms.

• Shape user onboarding, progressive disclosure, and feedback loops to enhance the accessibility and trustworthiness of AI-powered tools for a diverse range of user personas.

📝 Enhancement Note: The role is positioned as an "AI Engineer - Full Stack Agentic UX," indicating a strong emphasis on combining software engineering expertise with a deep understanding of user experience for AI-driven systems. The "Agentic UX" component suggests a focus on designing interactions with AI agents that exhibit autonomy and perform complex tasks, particularly within the context of financial services where trust and governance are paramount. The estimated experience level of 5-10 years is derived from the technical depth and strategic impact expected from designing and building such advanced AI capabilities.

📈 Primary Responsibilities

• Architect and implement user-facing interaction patterns for AI agents, ensuring clarity, control, and trust for end-users in complex financial workflows.

• Develop and deploy AI-driven capabilities that automate multi-step processes, leveraging the enterprise AI platform's orchestration and governance features.

• Design and integrate trust mechanisms such as confidence scoring, action logging, and clear escalation paths to support explainable AI and user confidence.

• Build seamless integrations with existing enterprise systems (e.g., JIRA, Slack, Git) to embed AI capabilities into daily workflows and enhance productivity.

• Craft intuitive onboarding flows and user guidance to make advanced AI tools accessible and valuable to users with varying technical and AI proficiencies.

• Establish and refine the capability development lifecycle, from concept and authoring to testing, validation, and deployment of AI-powered workflows.

• Design extensible frameworks that enable other teams and community members to create, share, and deploy custom AI capabilities on the platform.

• Collaborate closely with platform engineering teams to ensure seamless alignment between developed capabilities and the underlying AI orchestration, governance, and distribution infrastructure.

📝 Enhancement Note: The responsibilities highlight a blend of front-end and back-end development ("full-stack"), user experience design, and an understanding of AI/ML principles. The emphasis on "progressive autonomy," "intent previews," and "explainable rationale" points towards a need for sophisticated UX design for AI, going beyond simple conversational interfaces. The mention of "regulated financial services environment" implies a critical need for robust governance, security, and compliance in all developed solutions.

🎓 Skills & Qualifications

Education: While not explicitly stated, a Bachelor's or Master's degree in Computer Science, Engineering, Human-Computer Interaction, or a related field is typically expected for this level of role.

Experience: 5-10 years of professional software engineering experience, with a significant portion focused on full-stack development and user-facing applications. Experience in designing and implementing AI-powered features or complex workflow automation is highly valued.

Required Skills:

• Proven full-stack engineering expertise in Python and/or TypeScript, with a strong track record of building production-grade, user-facing tools and integrations.

• Demonstrated UX design sensibility and the ability to translate intricate agentic workflows into intuitive, accessible interaction patterns for diverse user personas.

• Solid understanding of human-agent interaction principles, including trust calibration, progressive autonomy, transparency, and maintaining user control in AI-driven experiences.

• Familiarity with AI/LLM-powered tools, prompt engineering techniques, and the development patterns of agentic AI.

• Experience in building robust integrations with enterprise APIs using protocols such as REST, GraphQL, and SOAP.

• Ability to effectively collaborate with cross-functional teams, including solution architects, business analysts, and project managers. Preferred Skills:

• Prior experience designing and developing AI-powered products like copilots, agents, or intelligent workflow tools where user trust and transparency are paramount.

• Background in design systems, component architecture, or creating AI-consumable design patterns.

• Experience within financial services technology or other highly regulated industries, with an understanding of governance and compliance requirements.

• Familiarity with developer experience (DevEx) tooling, internal developer platforms, or plugin ecosystem development.

📝 Enhancement Note: The "Must Have" skills strongly indicate a need for engineers who can bridge the gap between complex AI technology and user needs. The emphasis on "production-grade" implies experience with software development best practices, testing, and deployment in enterprise settings. The "Nice to Have" skills suggest areas where candidates can differentiate themselves, particularly those with domain experience in financial services or a deeper understanding of developer tooling.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Showcase projects demonstrating full-stack development capabilities, ideally with a focus on user-facing applications or complex system integrations.

• Include case studies of designing and implementing user interfaces or workflows for AI-driven features, highlighting how user trust and transparency were addressed.

• Present examples of API integrations, particularly with enterprise-level systems, demonstrating proficiency in connecting disparate platforms.

• Provide evidence of workflow automation projects, illustrating the ability to analyze processes, identify bottlenecks, and implement efficient solutions.

• Demonstrate understanding of AI/LLM concepts through personal projects or contributions to AI-related tools. Process Documentation:

• Candidates are expected to articulate their design and development process for AI features, from initial concept and user research through to implementation and iterative improvement.

• Evidence of creating clear documentation for APIs, integrations, or AI workflows will be beneficial.

• The ability to describe the lifecycle of a feature or capability, including testing, quality assurance, and deployment strategies, is important.

📝 Enhancement Note: For an "AI Engineer - Full Stack Agentic UX" role, a portfolio is crucial. It should not only display technical coding prowess but also demonstrate a thoughtful approach to user experience design, particularly for AI. The emphasis on "agentic" capabilities means showcasing how users interact with autonomous or semi-autonomous systems and how trust is built. Providing examples of process documentation and clear articulation of their development lifecycle will be key to demonstrating a structured and professional approach.

💵 Compensation & Benefits

Salary Range: Based on industry benchmarks for AI Engineers with 5-10 years of experience in major Canadian financial hubs like Toronto, the estimated annual salary range is CAD $120,000 - $180,000. This range accounts for the specialized skills in AI, full-stack development, and UX design, as well as the financial services industry context.

Benefits:

• Comprehensive Total Rewards Program including bonuses.

• Flexible benefits package tailored to employee needs.

• Competitive base compensation and potential for stock options.

• Opportunities for professional development through coaching and management.

• Access to a world-class training program in financial services.

• Flexible work/life balance options to support employee well-being.

• Opportunities to work on challenging and impactful projects.

Working Hours: 37.5 hours per week, standard full-time employment.

📝 Enhancement Note: The salary estimate is based on data from reputable compensation surveys for AI/ML Engineers and Full-Stack Developers in Canada, adjusted for the specific requirements of the role (AI, UX, enterprise integration) and the company's industry (large financial institution). The benefits listed are directly extracted from the job description and highlight RBC's commitment to employee well-being and professional growth.

🎯 Team & Company Context

🏢 Company Culture

Industry: Financial Services (Banking and Financial Technology). RBC is one of Canada's largest and most diversified financial services companies, operating globally. This industry context implies a strong focus on security, compliance, data privacy, and customer trust.

Company Size: Large Enterprise (RBC is a global financial institution with tens of thousands of employees). This means opportunities for working on large-scale, impactful projects with extensive resources, but also potentially more structured processes and a need for adaptability within a large organization.

Founded: 1864 (Royal Bank of Canada). This long history signifies stability, a deep understanding of the financial market, and a commitment to long-term innovation.

Team Structure:

• The role likely sits within a dedicated AI platform or advanced technology group, focusing on building the user-facing components of an enterprise AI platform.

• This team will collaborate closely with core AI platform engineers, data scientists, product managers, and business stakeholders across various RBC divisions.

• Reporting structure is likely within a technology or digital innovation department, with clear lines of reporting and project management. Methodology:

• Emphasis on agile development methodologies for rapid iteration and continuous delivery of AI capabilities.

• Data-driven decision-making, leveraging analytics to inform UX design, workflow optimization, and AI model performance.

• A strong focus on user-centered design principles, ensuring AI solutions are practical, trustworthy, and aligned with business objectives.

• Collaborative approach to problem-solving, encouraging cross-functional input and knowledge sharing.

Company Website: https://www.rbc.com/ (General Corporate) and https://jobs.rbc.com/ca/en (Careers)

📝 Enhancement Note: The company context emphasizes RBC's standing as a major global bank, which means candidates should anticipate working within a highly regulated and security-conscious environment. The "One RBC" culture mentioned in the broader job posting details suggests a focus on collaboration and shared values, which will be important for team integration. The AI platform team is likely at the forefront of technological innovation within the bank.

📈 Career & Growth Analysis

Operations Career Level: This "AI Engineer - Full Stack Agentic UX" role is positioned at a mid-to-senior engineering level. It requires not only strong technical execution but also strategic thinking in user experience design for advanced AI systems and the ability to influence how AI is integrated into enterprise workflows.

Reporting Structure: The role likely reports to an Engineering Lead or Manager within the AI platform or advanced technology division. Collaboration will be key, with frequent interaction with Product Managers, UX Designers, and other engineers.

Operations Impact: This role has a direct impact on enhancing operational efficiency and productivity across RBC by enabling practitioners to leverage advanced AI capabilities. The success of the AI platform is intrinsically tied to the user experiences designed and built, directly influencing adoption rates and the realization of business value from AI investments.

Growth Opportunities:

• Technical Specialization: Deepen expertise in AI/ML, agentic systems, LLM integration, and full-stack development for complex enterprise applications.

• UX for AI Leadership: Grow into a leadership role focused on defining best practices for human-agent interaction and AI product design within a major financial institution.

• Cross-Functional Mobility: Opportunity to move into product management, solution architecture, or team leadership roles within the AI and digital innovation space at RBC.

• Domain Expertise: Develop specialized knowledge in financial services technology and AI applications within regulated industries.

📝 Enhancement Note: The growth opportunities highlight that this role is more than just coding; it's about shaping the future of AI interaction within a large enterprise. The emphasis on UX for AI suggests a path towards specialized leadership in a rapidly evolving field. The structured environment of RBC also provides clear pathways for career progression within established corporate frameworks.

🌐 Work Environment

Office Type: Primarily on-site, with specific locations mentioned as RBC CENTRE, 155 WELLINGTON ST W:TORONTO and potential for Vancouver. This indicates a traditional office-based work environment, fostering in-person collaboration and team cohesion.

Office Location(s):

• Toronto, Ontario: RBC CENTRE, 155 WELLINGTON ST W. This is a prime downtown Toronto location, offering access to a major financial and business hub.

• Vancouver, British Columbia: The mention of Vancouver as an alternative location suggests RBC has significant operations there, potentially offering flexibility or a choice for candidates.

Workspace Context:

• Collaborative Environment: Expect a dynamic workspace designed to encourage interaction, brainstorming, and team-based problem-solving, typical of modern tech and finance offices.

• Technology Access: Employees will have access to robust IT infrastructure, development tools, and potentially specialized hardware or software required for AI development.

• Team Interaction: Frequent opportunities for direct interaction with colleagues, managers, and cross-functional team members, facilitating knowledge sharing and rapid feedback loops.

Work Schedule: 37.5 hours per week, with a standard work schedule. While primarily on-site, the mention of "Flexible work/life balance options" in the benefits suggests potential for some degree of flexibility in daily scheduling, where operationally feasible.

📝 Enhancement Note: The on-site requirement is a key differentiator for this role. Candidates should be prepared for a traditional office setting in a major Canadian city. The emphasis on collaboration within the workspace context suggests that in-person interaction is valued for innovation and team synergy.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A review of your resume and application for alignment with the required skills and experience, potentially followed by a brief HR or recruiter screening call.

• Technical Interview(s): Expect in-depth technical discussions focusing on your Python/TypeScript skills, full-stack architecture, API integration experience, and understanding of AI/LLM concepts. This may include coding challenges or live coding sessions.

• UX & AI Design Discussion: An interview focused on your approach to designing human-agent interactions, understanding of trust mechanisms, and experience with agentic AI principles. Be prepared to discuss your thought process and design rationale.

• Portfolio Presentation: A dedicated session where you will present selected projects from your portfolio, explaining the problem, your solution, your role, the technologies used, and the outcomes achieved.

• Behavioral & Situational Interviews: Questions assessing your ability to work in a team, handle complex challenges, communicate effectively, and align with RBC's values (Client First, Integrity, Collaboration, Respect, Excellence).

• Hiring Manager/Team Lead Interview: A final discussion to assess cultural fit, overall suitability for the team, and alignment with the strategic direction of the AI initiatives.

Portfolio Review Tips:

• Curate Strategically: Select 2-3 projects that best showcase your full-stack capabilities, AI/LLM exposure, and, most importantly, your UX design for AI skills. Prioritize projects demonstrating complex workflow automation or user interaction with intelligent systems.

• Highlight "Agentic UX": For each project, clearly articulate how you designed the user experience for AI agents. Discuss challenges related to trust, transparency, progressive autonomy, and how you addressed them. Use diagrams or mockups if possible.

• Quantify Impact: Whenever possible, use metrics to demonstrate the success of your projects. For example, improvements in efficiency, task completion rates, user satisfaction scores, or reduction in manual effort.

• Explain Your Process: Be ready to walk through your development and design process for each project, from requirements gathering and technical design to implementation and testing.

• Tailor to RBC: Briefly research RBC's current AI initiatives or digital transformation efforts to frame your experience and demonstrate your understanding of their context.

Challenge Preparation:

• Coding Challenges: Practice coding problems in Python and/or TypeScript, focusing on data structures, algorithms, and common web development patterns.

• System Design: Be prepared to discuss how you would design an AI-driven feature or integration, considering scalability, security, and user experience.

• AI/LLM Scenarios: Think about how you would approach common challenges in agentic AI development, such as prompt engineering for specific tasks, handling AI hallucinations, or implementing feedback loops.

📝 Enhancement Note: The interview process for such a specialized role will likely be rigorous, testing both technical depth and design thinking. A strong portfolio is essential for demonstrating practical application of skills, especially the unique "Agentic UX" aspect. Candidates should prepare to articulate their design decisions and problem-solving methodologies clearly and concisely.

🛠 Tools & Technology Stack

Primary Tools:

• Programming Languages: Python, TypeScript (required).

• Web Frameworks: Experience with frameworks like Flask, Django (Python) or Node.js/Express, React, Angular, Vue.js (TypeScript) is highly probable.

• AI/ML Libraries: Familiarity with libraries like TensorFlow, PyTorch, scikit-learn, Hugging Face Transformers, LangChain, or similar tools for LLM interaction and agent development.

• API Technologies: REST, GraphQL, SOAP (required for integrations).

Analytics & Reporting:

• Data Analysis Tools: Experience with tools for analyzing user behavior, AI performance metrics, and workflow efficiency (e.g., Pandas, NumPy, potentially BI tools like Tableau or Power BI for reporting).

• Monitoring & Logging: Tools for tracking application performance, identifying errors, and monitoring AI agent behavior in production.

CRM & Automation:

• Enterprise Systems Integration: Experience connecting with common enterprise platforms such as JIRA, Confluence, Slack, Microsoft Teams, Git repositories (GitHub, GitLab), and potentially internal banking systems.

• Cloud Platforms: While not explicitly stated, experience with cloud environments like Azure (given RBC's likely partnership), AWS, or GCP would be beneficial for deploying and scaling AI applications.

📝 Enhancement Note: The technology stack emphasizes modern full-stack development with a strong AI/ML component. The requirement for API integration with enterprise systems is critical. Candidates should be prepared to discuss their proficiency with specific libraries and frameworks relevant to building sophisticated AI applications and integrating them into a large corporate ecosystem.

👥 Team Culture & Values

Operations Values:

• Client First: All work should ultimately aim to improve client experience or provide value, even indirectly through internal process improvements.

• Integrity: Upholding ethical standards, data privacy, and compliance in all AI development and deployment, especially crucial in financial services.

• Collaboration: Working effectively across diverse teams (engineering, product, business, compliance) to achieve shared goals.

• Respect: Valuing diverse perspectives and fostering an inclusive environment where all team members feel heard and respected.

• Excellence: Striving for high-quality solutions, continuous improvement, and driving innovation in AI and user experience.

Collaboration Style:

• Cross-Functional Integration: Expect a highly collaborative environment where engineers work hand-in-hand with product managers, UX designers, data scientists, and business analysts to define and deliver AI solutions.

• Agile & Iterative: A culture that embraces agile methodologies, encouraging frequent feedback, iterative development, and adaptability to changing requirements.

• Knowledge Sharing: A strong emphasis on sharing best practices, code reviews, and learning from each other to collectively elevate the team's capabilities.

📝 Enhancement Note: RBC's stated values are central to their culture. For this role, demonstrating how one embodies "Integrity" and "Excellence" in the context of AI development, and how they foster "Collaboration" with diverse stakeholders, will be key to cultural fit. The "Client First" principle will guide the application of AI to solve real business problems.

⚡ Challenges & Growth Opportunities

Challenges:

• Balancing Innovation with Regulation: Navigating the complexities of developing cutting-edge AI features within a highly regulated financial services environment, ensuring compliance and security are paramount.

• Building Trust in AI: Designing user experiences that instill confidence and transparency in AI-driven workflows, overcoming potential user skepticism towards autonomous systems.

• Integration Complexity: Seamlessly integrating AI capabilities with a multitude of existing, often legacy, enterprise systems while maintaining data integrity and performance.

• Rapidly Evolving AI Landscape: Keeping pace with the fast-changing advancements in AI, LLMs, and agentic technologies while applying them to practical business problems.

Learning & Development Opportunities:

• AI Specialization: Opportunities to dive deep into advanced AI/ML techniques, LLM fine-tuning, and the architecture of agentic systems.

• Financial Services Domain Expertise: Gaining in-depth knowledge of the financial industry's specific needs, regulations, and technological challenges.

• Cross-Disciplinary Skill Building: Developing stronger skills in UX design for AI, product management, and enterprise-level system architecture.

• Leadership Development: Potential to grow into technical leadership roles, mentoring junior engineers, or leading specific AI initiatives within RBC.

📝 Enhancement Note: This role presents a unique opportunity to tackle significant challenges at the intersection of AI innovation and financial services. The growth path is clear for those who excel in this demanding yet rewarding environment.

💡 Interview Preparation

Strategy Questions:

• AI Strategy & Ethics: "How would you approach designing an AI agent for a critical financial process, considering potential biases, explainability, and user trust?" (Prepare to discuss your framework for ethical AI development and risk mitigation.)

• User-Centric AI Design: "Describe a situation where you had to translate a complex technical AI capability into a simple, user-friendly experience. What was your process?" (Focus on your UX design methodology and how you gather/apply user feedback.)

• Agentic Workflow Design: "Imagine building an AI agent to assist with compliance checks. What key interaction patterns would you implement to ensure the user remains in control and understands the AI's decisions?" (Think about progressive autonomy, previews, audit trails, and escalation paths.)

Company & Culture Questions:

• RBC's AI Vision: "Based on your understanding of RBC and the financial industry, where do you see the biggest opportunities for AI to drive value?" (Research RBC's stated digital transformation goals and recent AI news.)

• Team Collaboration: "How do you handle disagreements with team members or stakeholders regarding technical approaches or design decisions?" (Prepare examples demonstrating your collaborative problem-solving skills and alignment with RBC's values.)

• Impact Measurement: "How would you measure the success and impact of an AI-driven feature you've developed?" (Discuss key performance indicators (KPIs) relevant to efficiency, user adoption, and business outcomes.)

Portfolio Presentation Strategy:

• Storytelling: Frame each project as a narrative: the problem, your role and approach, the technical solution, the UX design choices (especially for AI), and the measurable impact.

• Visuals: Use clear diagrams, screenshots, or mockups to illustrate your designs and technical architecture. For AI projects, visually represent the interaction flows or agent logic.

• Focus on "Agentic UX": Explicitly detail how you designed for human-AI interaction, trust, and control. Be prepared to defend your UX decisions with user-centered reasoning.

• Technical Depth: Be ready to answer questions about your code, the technologies used, and architectural decisions made.

• Conciseness: Respect the allocated time. Practice your presentation to be clear, engaging, and within limits.

📝 Enhancement Note: Interview preparation should focus on demonstrating a blend of strong technical skills, strategic thinking in AI UX, and an understanding of the financial services context. The portfolio presentation is a critical component, serving as a tangible demonstration of your capabilities.

📌 Application Steps

To apply for this AI Engineer position:

• Submit your application through the RBC careers portal using the provided link.

• Optimize Your Resume: Tailor your resume to highlight your Python/TypeScript, full-stack, AI/LLM, and UX design experience. Quantify achievements with metrics wherever possible, especially those related to workflow automation or user experience improvements.

• Curate Your Portfolio: Select 2-3 key projects that best showcase your "Agentic UX" design skills, full-stack development capabilities, and experience with AI/LLM technologies. Ensure your portfolio clearly articulates your role, the problem solved, and the impact.

• Prepare Your Presentation: Practice walking through your selected portfolio projects, focusing on your design process, technical approach, and how you built trust and transparency in AI interactions.

• Research RBC: Familiarize yourself with RBC's values, its position in the financial industry, and any publicly available information on its AI or digital transformation initiatives to tailor your responses and demonstrate genuine interest.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.