Lead, Full Stack Engineer

Role overview

Responsibilities

• Apply AI and agentic tools to solve business problems across US Corporate Functions.
• Migrate .NET applications from on-premises to Azure and design reliable, secure, scalable hybrid solutions.
• Plan and deliver application integrations from conception through production, including AI where valuable.
• Design and build APIs and web services; integrate SSO, security, and Kafka or MQ messaging.
• Lead CNB entity integration with Target Tech teams and support seamless business adoption.
• Consolidate CNB Finance and Audit Tech applications onto the RBC Common Platform with business stakeholders.
• Carry out Python scripting and data pipeline development.
• Lead, mentor, and grow engineers; set technical direction and quality standards.
• Gather stakeholder requirements and align AI initiatives with strategic goals.
• Develop and deploy cloud-native AI/ML solutions on AWS, Azure, or GCP using Kubernetes and Docker.
• Maintain working knowledge of shared services, Splunk, Elastic, and performance monitoring.

What you need to succeed

• 7+ years leading an engineering team while remaining hands-on.
• Software engineering experience in at least one of .NET, Java, or Python.
• Experience building and integrating APIs and web services across SSO, security, and Kafka or MQ messaging.
• Experience migrating on-premises applications to Azure, AWS, or GCP and operating hybrid solutions.
• Ability to translate business requirements into technical roadmaps and identify strategic AI opportunities.
• Strong data skills, including ETL, data orchestration, Python scripting, or data pipeline development.
• Experience deploying cloud-native solutions with Kubernetes and Docker, and exposure to production AI/ML.
• Experience delivering integration plans to committed timelines and managing architecture changes.
• Ability to partner with business and enterprise stakeholders, gather requirements, and align technical work with strategic goals.

Preferred experience

• Cloud AI application scaling, model optimization, latency reduction, and performance monitoring.
• RAG systems and vector databases for knowledge retrieval.
• Prompt engineering and AI-based SDLC tools such as Claude or GitHub Copilot.
• Python AI frameworks such as LangChain, AutoGen, or CrewAI; AI agent architectures and multi-agent collaboration.

What RBC offers

• Total Rewards Program with bonuses and flexible benefits; competitive compensation, commissions, and stock where applicable.
• Coaching and management opportunities, challenging work, and a collaborative team environment.

Work location

• The posting lists a Vancouver, British Columbia address; it does not explicitly state an onsite, hybrid, or remote work arrangement.