Lead, Full Stack Engineer

Role overview

Engineering and modernization

• Apply AI and agentic tools to business problems; migrate .NET applications from on-premises to Azure and design secure, scalable hybrid solutions.
• Develop integration plans from conception through production deployment, including AI solutions where useful and delivery to committed timelines.
• Architect APIs, web services, SSO, security, and Kafka or MQ messaging with focus on performance, security, and compliance.
• Build and maintain backend APIs and web services; integrate systems across identity, security, and messaging.
• Lead CNB entity integration and adoption; consolidate CNB Finance and Audit applications onto the target RBC common platform.
• Build data pipelines and perform data work, including Python scripting.
• Lead and mentor engineers, establish technical direction and quality standards, and guide stakeholder discussions and requirements.
• Develop cloud-native AI/ML solutions on AWS, Azure, or GCP using technologies such as Kubernetes and Docker.

Required qualifications

• At least 7 years leading an engineering team, while remaining hands-on and unblocking delivery.
• Software engineering background in at least one of .NET, Java, or Python.
• Experience integrating APIs and web services across SSO, security, and Kafka or MQ messaging; ability to make architecture decisions for performance, security, and compliance.
• Experience migrating on-premises applications to Azure, AWS, or GCP and designing hybrid solutions.
• Translate business needs into technical roadmaps and identify AI transformation opportunities aligned with enterprise strategy.
• Strong data experience in ETL, orchestration, Python scripting, or data pipelines.
• Experience deploying cloud-native solutions with Kubernetes and Docker, with exposure to production AI/ML.
• Experience delivering integration plans on committed timelines and managing architecture changes; able to partner with stakeholders and gather requirements.

Preferred AI experience

• Scaling cloud AI applications, model optimization, latency reduction, and performance monitoring are beneficial.
• RAG systems, vector databases, prompt engineering, AI-based SDLC tools, Python AI frameworks such as LangChain, AutoGen, or CrewAI, and multi-agent architectures are nice to have.

Rewards and growth

• Comprehensive total rewards program, including bonuses and flexible benefits; compensation, commissions, and stock where applicable.
• Coaching and development opportunities, collaborative work, and opportunities to make a lasting impact.

Work location and schedule

• The posting lists a Vancouver, British Columbia address and 37.5 work hours per week.

Employment details