Lead, Full Stack Engineer

The Lead, Full Stack Engineer is a hands-on leadership role within AI Engineering, US Functions Technology – Finance, Risk, Law, and Audit. You will build and inspire a team that delivers scalable, production-ready solutions. You will lead modernizing .NET applications, bring AI and agentic tools into everyday engineering, and drive CNB US Functions technology into the RBC Enterprise ecosystem, owning every modernization solution from concept to production. Responsibilities Innovate at the forefront, applying cutting-edge technologies, including AI and agentic tools, to solve real-world problems across US Corporate Functions, including Finance, Risk, Law, and Audit Drive the migration of .NET applications from on-premises to Azure, designing and building hybrid solutions that remain reliable, secure, and scalable across both environments Provide hands-on development and management of application integration plans, incorporating AI solutions where they add value, from conception through production deployment, with committed delivery timelines. Drive solution architecture and design for APIs, web services, SSO and security, and messaging (Kafka or MQ), with a focus on performance, security, and compliance Build and maintain back-end APIs and web services, delivering heavy integration work across SSO and security, and messaging through Kafka or MQ. Drive and lead the integration of CNB entities, ensuring seamless business adoption of target solutions in collaboration with Target Tech teams Consolidate applications from CNB Finance & Audit Tech to Target RBC Common Platform through strategic alignment with business stakeholders Carry out substantial data work, including scripting (Python) and data pipeline development Lead, mentor, and grow a team of engineers setting technical direction and quality standards. Maintain a working understanding of enterprise operations, including shared services, Splunk, Elastic, and performance monitoring Champion cross-functional collaboration by leading discussions with business stakeholders to gather requirements, refine visions, and align AI initiatives with strategic goals to translate into actionable AI strategies Develop and deploy cloud-native AI/ML solutions using AWS, Azure, or GCP, ensuring scalability, reliability, and efficiency through Kubernetes, Docker, etc. Requirements 7+ years leading an engineering team, staying hands-on and stepping in to unblock delivery when needed Software engineering background with at least one of the following languages: .NET, Java, or Python Solid experience building and integrating APIs and web services across SSO, security, and messaging through Kafka or MQ, with the ability to drive architecture and design decisions focused on performance, security, and compliance Proven experience migrating applications from on-premises to cloud platforms (Azure, AWS, or GCP) and designing and running hybrid solutions across both environments Skilled at translating business requirements into technical roadmaps, and identify AI-driven transformation opportunities aligned with enterprise strategy Strong data skills, including experience with ETL, data orchestration, Python scripting, or building data pipelines Experience developing and deploying cloud-native solutions using Kubernetes and Docker, with exposure to AI or ML solutions in production Experience delivering integration plans to committed timelines, including managing architecture changes Ability to partner with enterprise and business stakeholders, gather requirements, and align technical work with strategic goals Nice to Have Experience scaling and deploying AI applications in cloud environments with model optimization, latency reduction, and performance monitoring Experience with Retrieval-Augmented Generation (RAG) systems and vector databases for knowledge retrieval Solid foundation in prompt engineering and AI based SDLC (Claude, GitHub Copilot) Familiarity with Python and AI frameworks (LangChain, AutoG