Principal Data Science and AI Engineer

Job Description

What is the opportunity?

Are you ready to lead transformative AI initiatives and drive innovation at enterprise scale? Do you excel in environments of rapid technological advancement and thrive on solving complex, high-impact challenges? Are you passionate about shaping the future of businesses through cutting-edge AI solutions and influencing organizational strategy?

As a Principal AI Engineer, you will be at the helm of developing, deploying, and owning the strategic direction of advanced AI systems, including autonomous AI agents that leverage and fine-tune large language models (LLMs) and other deep learning technologies. This role offers a unique opportunity to define the technical roadmap, drive enterprise-wide adoption, and lead the design and implementation of intelligent systems that redefine how we operate and deliver value. You will provide technical leadership across multiple squads and domains, advise senior leadership on AI strategy and investment priorities, and be accountable for the measurable business impact of AI platform capabilities. This is your chance to engage with state-of-the-art AI technologies, influence organizational direction at the executive level, and make a lasting impact on the industry.This role is designed for a visionary technical leader who is passionate about advancing AI technologies and driving meaningful, measurable change. If you are ready to take on this challenge, we invite you to join us and shape the future of AI and Advice Center at RBC.

What will you do?

Lead the Design and Development of Platform Services for Autonomous AI Agents

Engineer, design, and enable the development of platform services that enable autonomous AI agents to interact with infrastructure systems like OpenShift, SQL Server, Apigee, Kafka, Elasticsearch, etc.

Design APIs and natural language interfaces for AI agents to perform tasks like diagnostics, remediation, and maintenance.

Integrate data (Metrics, Events, Logs, Traces) to power AI-driven anomaly detection and automation.

Drive AI experiments from proof-of-concept through production deployment, establishing LLMOps practices, model governance, and lifecycle management frameworks.

Strategic Ownership and Roadmap

Define and own the 12–18 month technical roadmap for AI platform services, aligning with organizational priorities and business objectives.

Evaluate build-vs-buy decisions for AI infrastructure components, tooling, and vendor selection.

Influence organizational investment priorities through technical analysis, competitive landscape assessment, and strategic recommendations.

Accountable for the success, adoption, and ROI of AI platform capabilities across the enterprise.

Define and track KPIs for AI-driven solutions, reporting outcomes to senior leadership.

AI-Driven Innovation to Serve Intelligent Advice Center

Optimize platform services for generative AI models and autonomous workflows.

Apply AI techniques like reinforcement learning and transfer learning to enhance Advisor and Client experience.

Experiment with emerging AI technologies to elevate digital channels, advisor and client interaction management and optimize client experience.

Establish standards for responsible AI, model governance, and ethical AI practices across the organization.

Provide Technical Leadership and Executive Influence

Lead architecture design, code reviews, and technical discussions for high-quality platform solutions.

Bridge platform teams and AI squads to ensure seamless integration and alignment across multiple domains.

Promote best practices for platform management, AI integration, and automation across teams.

Make high-impact technical decisions on architecture, tooling, and vendor selection with enterprise-wide implications.

Act as the final technical escalation point for complex AI system issues.

Own technical risk assessment for AI initiatives and communicate risk posture to leadership.

Advise senior leadership (VP+/SVP) on AI strategy, capabilities, and emerging opportunities.

Present to executive steering committees on technical direction, progress, and business impact.

Ensure Operational Excellence

Implement monitoring frameworks to ensure reliable AI-driven workflows at enterprise scale.

Address scalability and resilience challenges in advice centers integrated with AI agents.

Enforce security, compliance, and data privacy standards across platform operations for AC.

Establish SLAs and reliability targets for AI-powered systems, ensuring accountability for uptime and performance.

Talent Development and Organizational Impact

Mentor team members, fostering a culture of innovation and technical excellence across the engineering organization.

Participate in hiring decisions and shape talent strategy for AI engineering teams.

Define competency frameworks and career paths for AI engineers at all levels.

Raise the technical bar across the engineering organization through standards, guidelines, and technical leadership.

Document and share knowledge on platform enablement and AI integration processes.

Represent DPD team in internal and external forums, showcasing the impact of platform initiatives.

Publish thought leadership, contribute to internal tech radar, and represent the organization at industry conferences.

What do you need to succeed?

Must-Have:

Specialized AI Skills:

Agentic AI Architecture — Design and orchestrate multi-step autonomous AI agents with tool use, memory, planning, and guardrails (e.g., ReAct, function calling, agent loops, human-in-the-loop patterns)

LLM Engineering & Optimization — Prompt engineering at scale, RAG pipeline design, fine-tuning, embedding strategies, context window management, model evaluation, and cost/latency optimization for production workloads, vector databases, knowledge graphs, data chunking strategies, grounding datasets, and feedback loops for continuous model improvement

AI Strategy & Enterprise Enablement — Translate business problems into AI solution architectures, evaluate model/vendor trade-offs (open-source vs. proprietary, build vs. buy), define responsible AI standards, and drive organizational AI adoption beyond individual productivity

Educational Background:

Bachelor's or Master's or preferably PhD degree in Computer Science, Engineering, Mathematics, or related field.

Software Development Expertise:

Proficiency in Python, Java, or C++ with a strong grasp of software engineering principles.

Platform Integration Knowledge:

Experience with enterprise platforms like OpenShift, SQL Server, Apigee, Kafka, Elasticsearch, and cloud environments (AWS, Azure, GCP).

Familiarity with AC tools, Digital and Conversational banking and automated operations workflows.

AI Integration Skills:

Hands-on experience with generative AI and large language models (LLMs).

Expertise in NLP techniques and programmatic API usage of LLMs.

Experience establishing LLMOps pipelines and model lifecycle management.

Strategic and Business Acumen:

Demonstrated ability to translate technical capabilities into business outcomes and ROI.

Experience defining technical roadmaps and influencing investment decisions.

Track record of delivering AI initiatives with measurable enterprise-wide impact.

Leadership and Collaboration:

Strong communication skills to simplify technical concepts for executive and non-technical audiences.

Proven ability to lead agile teams and drive cross-functional collaboration across multiple squads and domains.

Experience advising VP+ leadership and presenting to executive steering committees.

Track record of hiring, developing, and retaining top engineering talent.

Nice to Have:

Expertise in reinforcement learning, autonomous agents, or advanced AI techniques.

Familiarity with infrastructure-as-code tools like Terraform, Kubernetes, and CI/CD